Posted: August 6th, 2022

Eco Case Study

The Global Financial Stability Report is a semiannual report published by the International Capital Markets division of the International Monetary Fund. The report includes an assessment of the risks facing the global financial markets. Locate and download (see attached) the latest report to get an overview of the most important issues currently under discussion. Also, download a report from five years ago (see attached).

How do issues from five years ago compare with financial issues identified in the current report?

Also, cite peer-reviewed sources to support the answer. Need 4-5 pages. Need introduction and conclusion as well.

2022
APR
GLOBAL
FINANCIAL
STABILITY
REPORT
Shockwaves from the War in Ukraine
Test the Financial System’s Resilience
INTERNATIONAL MONETARY FUND

2022
APR
GLOBAL
FINANCIAL
STABILITY
REPORT
INTERNATIONAL MONETARY FUND
Shockwaves from the War in Ukraine
Test the Financial System’s Resilience

©2022 International Monetary Fund
IMF CSF Creative Solutions Division
Composition: AGS, An RR Donnelley Company
Cataloging-in-Publication Data
IMF Library
Names: International Monetary Fund.
Title: Global financial stability report.
Other titles: GFSR | World economic and financial surveys, 0258-7440
Description: Washington, DC : International Monetary Fund, 2002- | Semiannual | Some issues also have thematic
titles. | Began with issue for March 2002.
Subjects: LCSH: Capital market—Statistics—Periodicals. | International finance—Forecasting—Periodicals. |
Economic stabilization—Periodicals.
Classification: LCC HG4523.G557
ISBN 979-8-40020-529-3 (Paper)
979-8-40020-559-0 (ePub)
979-8-40020-577-4 (PDF)
Disclaimer: The Global Financial Stability Report (GFSR) is a survey by the IMF staff published twice
a year, in the spring and fall. The report draws out the financial ramifications of economic issues high-
lighted in the IMF’s World Economic Outlook (WEO). The report was prepared by IMF staff and has
benefited from comments and suggestions from Executive Directors following their discussion of the
report on April 11, 2022. The views expressed in this publication are those of the IMF staff and do not
necessarily represent the views of the IMF’s Executive Directors or their national authorities.
Recommended citation: International Monetary Fund. 2022. Global Financial Stability Report—Shockwaves
from the War in Ukraine Test the Financial System’s Resilience. Washington, DC, April.
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International Monetary Fund | April 2022 iii
CONTENTS
Assumptions and Conventions vi
Further Information vii
Preface viii
Foreword ix
Executive Summary xi
IMF Executive Board Discussion of the Outlook, April 2022 xv
Chapter 1 The Financial Stability Implications of the War in Ukraine 1
Chapter 1 at a Glance 1
The War in Ukraine Raises Immediate Financial Stability Risks and
Questions about the Longer-Term Impact on Markets 1
Implications of Higher Commodity Prices for Monetary Policy 7
Transmission Channels of the War through Financial Intermediaries and Markets 12
Emerging Markets Have Come under Pressure, with Notable Differences across Countries 24
Financial Vulnerabilities Remain Elevated in China amid Ongoing Stress in the
Property Development Sector and COVID-19 Risks 29
Selected Medium-Term Structural Challenges Policymakers Will Need to Confront 30
Policy Recommendations 34
Policy Recommendations to Address Specific Financial Stability Risks 35
Box 1.1. Extreme Volatility in Commodities: The Nickel Trading Suspension 38
References 40
Chapter 2 The Sovereign-Bank Nexus in Emerging Markets: A Risky Embrace 41
Chapter 2 at a Glance 41
Introduction 41
Sovereign-Bank Interlinkages: Conceptual Framework 46
Relevance of the Sovereign-Bank Nexus in Emerging Markets: Some Stylized Facts 47
Deepening of the Sovereign-Bank Nexus during the COVID-19 Pandemic 50
Measuring the Strength of the Sovereign-Bank Nexus 50
Evidence about the Transmission Channels 52
Conclusion and Policy Recommendations 59
Box 2.1. The Drivers of Banks’ Sovereign Debt Exposure in Emerging Markets 62
References 64
Chapter 3 The Rapid Growth of Fintech: Vulnerabilities and Challenges for Financial Stability 65
Chapter 3 at a Glance 65
Introduction 65
Fintechs in Banking: Conceptual Framework and Risks 67
Case Study: Neobanks 68
Case Study: Fintechs in the US Home Mortgage Market 72
Decentralized Finance: Vulnerable Efficiency 73
Financial Stability and Policy Issues 81
References 83

G LO B A L F I N A N C I A L S TA B I L I T Y R E P O R T: S H O C K WAV E S F R O M T H E WA R I N U K R A I N E T E S T T H E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
iv International Monetary Fund | April 2022
Tables
Table 3.1. Comparison of Decentralized Finance and Traditional Financial Services 75
Figures
Figure 1.1. Russian and Ukrainian Assets Have Come under Heavy Pressure Following the
War in Ukraine 3
Figure 1.2. Impact of the War in Ukraine on Commodities 4
Figure 1.3. Impact of the War in Ukraine on Financial Assets 5
Figure 1.4. Financial Market Volatility Has Picked Up Dramatically 6
Figure 1.5. Global Financial Conditions 6
Figure 1.6. Global Growth-at-Risk 7
Figure 1.7. Drivers of Advanced Economy Bond Yields 8
Figure 1.8. Increase in Advanced Economy Policy Rates 10
Figure 1.9. A Challenging Normalization Process 11
Figure 1.10. Inflation and Interest Rates in Emerging Markets 12
Figure 1.11. Foreign Bank Exposures to Russia and Ukraine 14
Figure 1.12. Over-the-Counter Derivative Exposures of International and Domestic Banks in
Russia, End-2021 15
Figure 1.13. Exposure to Russian Assets by Foreign Nonbank Financial Intermediaries 16
Figure 1.14. Investor Challenges in Russian Security Markets 18
Figure 1.15. Impact from Russia’s Exclusion from Global Benchmark Indices 19
Figure 1.16. Commodity Trading Companies Have Been Exposed to a Spike in Volatility 20
Figure 1.17. Short-Term Dollar Funding Tensions and Market Liquidity 21
Figure 1.18. Corporate Sector amid the War in Ukraine 23
Figure 1.19. Emerging Market Financial Spillovers 25
Figure 1.20. Emerging Market Portfolio Flow Pressures Have Intensified 27
Figure 1.21. Crypto Asset Markets 28
Figure 1.22. Stress in the Chinese Property Development Sector 30
Figure 1.23. Chinese Property Development Spillovers 31
Figure 1.24. The War in Ukraine Tests the Climate Challenge 32
Figure 1.1.1. The Nickel Market Short Squeeze in March 2022 39
Figure 2.1. Developments in Emerging Market Public Debt and Banks’ Sovereign Exposures 42
Figure 2.2. Fiscal Vulnerabilities in Emerging Markets 44
Figure 2.3. Banks’ Exposure to Sovereign Debt in Emerging Markets 45
Figure 2.4. Key Channels of the Sovereign-Bank Adverse Feedback Loop 46
Figure 2.5. Association between Emerging Market Sovereign and Banking Sector
Default Risk 48
Figure 2.6. Sovereign Debt and Banking Crises in a Historical Context:
Emerging Markets versus Advanced Economies 49
Figure 2.7. Sovereign-Bank Nexus in Emerging Markets during the COVID-19 Pandemic 51
Figure 2.8. Transmission of Risks through the Sovereign-Bank Nexus:
Strength of the Main Channels across Emerging Markets 52
Figure 2.9. Sovereign and Bank Default Risk and Tightening of Global Financial
Conditions in Emerging Markets 53
Figure 2.10. Transmission of Sovereign Risk through the Exposure Channel 54
Figure 2.11. The Banking Sector Safety Net in Emerging Market Economies 56
Figure 2.12. The Effects of Sovereign Downgrades on Firms 58
Figure 2.1.1. Bank Holdings of Sovereign Debt 62
Figure 2.1.2. Drivers of Bank Holdings of Sovereign Debt in Emerging Markets 63
Figure 3.1. The Rise of Fintech Firms and Decentralized Finance 66
Figure 3.2. Fintechs in the Core Banking Intermediation Chain 68
Figure 3.3. The Increasing Relevance of Neobanks 69
Figure 3.4. Client Profile of Neobanks 70
Figure 3.5. Credit Risk Profile 71

C o n t e n t s
International Monetary Fund | April 2022 v
Figure 3.6. Margins, Profitability, and Liquidity Profiles of Neobanks 72
Figure 3.7. Fintechs in the US Home Mortgage Market 74
Figure 3.8. Recent Development of DeFi Lending 76
Figure 3.9. Decentralized Finance Market Risks 77
Figure 3.10. Decentralized Finance Liquidity Risks 78
Figure 3.11. Cyberattacks on Decentralized Finance 79
Figure 3.12. Efficiency and Risks of Decentralized Finance 80
Online Boxes and Annexes
Online Box 1.1. Indicator-Based Framework Update
Online Annex 2.1. Data Sources and Sample
Online Annex 2.2. The Role of Nonbank Financial Institutions in the Nexus
Online Annex 2.3. Additional Stylized Facts
Online Annex 2.4. The Drivers of Banks’ Holdings of Sovereign Debt in Emerging Markets
Online Annex 2.5. Measuring the Strength of the Nexus
Online Annex 2.6. Exposure Channel Analysis
Online Annex 2.7. Safety Net Channel Analysis
Online Annex 2.8. The Macroeconomic Channel Analysis
Online Annex 3.1. Case Study on Neobanks
Online Annex 3.2. Case Study: US Mortgage Market
Online Annex 3.3. Risk Analysis on DeFi Lending
Online Annex 3.4. Efficiency Analysis on Financial Institutions and DeFi Platforms
Editor’s Note (May 18, 2022)
This online version of the GFSR has been updated to reflect the following changes to the version
published online on April 13, 2022:
– Chapter 3, Figure 3.11, panel 1 subtitle on page 79: “(Billions of US dollars)” was corrected to
“(Millions of US dollars)”
– Chapter 2, Box 2.1, on page 62: country labels in Figure 2.1.1 were amended and the last three
countries mentioned in the second sentence of the first paragraph were corrected to “China,
Hungary, and Pakistan.”

vi International Monetary Fund | April 2022
ASSUMPTIONS AND CONVENTIONS
The following conventions are used throughout the Global Financial Stability Report (GFSR):
. . . to indicate that data are not available or not applicable;
— to indicate that the figure is zero or less than half the final digit shown or that the item does not exist;
– between years or months (for example, 2021–22 or January–June) to indicate the years or months covered,
including the beginning and ending years or months;
/ between years or months (for example, 2021/22) to indicate a fiscal or financial year.
“Billion” means a thousand million.
“Trillion” means a thousand billion.
“Basis points” refers to hundredths of 1 percentage point (for example, 25 basis points are equivalent to ¼ of
1 percentage point).
If no source is listed on tables and figures, data are based on IMF staff estimates or calculations.
Minor discrepancies between sums of constituent figures and totals shown reflect rounding.
As used in this report, the terms “country” and “economy” do not in all cases refer to a territorial entity that is a state
as understood by international law and practice. As used here, the term also covers some territorial entities that are
not states but for which statistical data are maintained on a separate and independent basis.
The boundaries, colors, denominations, and any other information shown on the maps do not imply, on the part
of the International Monetary Fund, any judgment on the legal status of any territory or any endorsement or
acceptance of such boundaries.

International Monetary Fund | April 2022 vii
FURTHER INFORMATION
Corrections and Revisions
The data and analysis appearing in the Global Financial Stability Report are compiled by the IMF staff at the
time of publication. Every effort is made to ensure their timeliness, accuracy, and completeness. When errors are
discovered, corrections and revisions are incorporated into the digital editions available from the IMF website and
on the IMF eLibrary (see below). All substantive changes are listed in the online table of contents.
Print and Digital Editions
Print
Print copies of this Global Financial Stability Report can be ordered from the IMF bookstore at imfbk.st/516157.
Digital
Multiple digital editions of the Global Financial Stability Report, including ePub, enhanced PDF, and HTML,
are available on the IMF eLibrary at www.elibrary.imf.org/APR22GFSR.
Download a free PDF of the report and data sets for each of the charts therein from the IMF website at
www.imf.org/publications/gfsr or scan the QR code below to access the Global Financial Stability Report web page
directly:
Copyright and Reuse
Information on the terms and conditions for reusing the contents of this publication are at www.imf.org/
external/terms.htm.

viii International Monetary Fund | April 2022
PREFACE
The Global Financial Stability Report (GFSR) assesses key vulnerabilities the global financial system is exposed
to. In normal times, the report seeks to play a role in preventing crises by highlighting policies that may mitigate
systemic risks, thereby contributing to global financial stability and the sustained economic growth of the IMF’s
member countries.
The analysis in this report was coordinated by the Monetary and Capital Markets (MCM) Department under
the general direction of Tobias Adrian, Director. The project was directed by Fabio Natalucci, Deputy Director;
Ranjit Singh, Assistant Director; Nassira Abbas, Deputy Division Chief; Antonio Garcia Pascual, Deputy Division
Chief; Evan Papageorgiou, Deputy Division Chief; Mahvash Qureshi, Division Chief; and Jérôme Vandenbussche,
Deputy Division Chief. It benefited from comments and suggestions from the senior staff in the MCM
Department.
Individual contributors to the report were Jose Abad, Sergei Antoshin, Parma Bains, Liumin Chen,
Yingyuan Chen, Fabio Cortes, Reinout De Bock, Andrea Deghi, Mohamed Diaby, Dimitris Drakopoulos, Tor-
sten Ehlers, Salih Fendoglu, Charlotte Gardes-Landolfini, Deepali Gautam, Rohit Goel, Sanjay Hazarika, Frank
Hespeler, Henry Hoyle, Shoko Ikarashi, Tara Iyer, Phakawa Jeasakul, Esti Kemp, Oksana Khadarina, Sheheryar
Malik, Fabiana Melo, Junghwan Mok, Kleopatra Nikolaou, Natalia Novikova, Thomas Piontek, Patrick Schneider,
Nobuyasu Sugimoto, Hamid Reza Tabarraei, Tomohiro Tsuruga, Jeffrey David Williams, Hong Xiao, Yizhi Xu,
Dmitry Yakovlev, Mustafa Yenice, Akihiko Yokoyama, Zhichao Yuan, and Xingmi Zheng. Javier Chang, Monica
Devi, Olga Tamara Maria Lefebvre, and Srujana Sammeta were responsible for word processing.
Gemma Rose Diaz from the Communications Department led the editorial team and managed the report’s
production with editorial assistance from David Einhorn, Harold Medina (and team), Lucy Scott Morales,
Nancy Morrison, Grauel Group, and TalentMEDIA Services.
This issue of the GFSR draws in part on a series of discussions with banks, securities firms, asset management
companies, hedge funds, standard setters, financial consultants, pension funds, trade associations, central banks,
national treasuries, and academic researchers.
This GFSR reflects information available as of April 7, 2022. The report benefited from comments and sugges-
tions from staff in other IMF departments, as well as from Executive Directors following their discussions of the
GFSR on April 11, 2022. However, the analysis and policy considerations are those of the contributing staff and
should not be attributed to the IMF, its Executive Directors, or their national authorities.

International Monetary Fund | April 2022 ix
FOREWORD
T
he backdrop of this Global Financial
Stability Report is a challenging one. Rising
risks to the inflation outlook and rapidly
changing views about the likely pace of
monetary policy tightening have been dominant
themes affecting financial stability. Juxtaposed against
financial stability risks is the Russian invasion of
Ukraine, which will exert a material drag on the
global recovery and pose significant uncertainties to
the outlook. The balance of risks to growth has tilted
more firmly to the downside as outlined in the April
2022 World Economic Outlook. These developments
have occurred just as the world is slowly bringing the
pandemic under control and as the global economy
continues to recover from COVID-19.
The sharp rise in commodity prices—in concert
with more prolonged supply disruptions—have
exacerbated preexisting inflation pressures and led to a
significant rise in inflation expectations. Central banks
face heightened challenges in credibly bringing infla-
tion to target while safeguarding economic recovery.
They will have to navigate a delicate balancing act
between removing accommodation at a pace that
prevents an unmooring of inflation expectations while
avoiding a disorderly tightening of financial condi-
tions that could interact with financial vulnerabilities
and weigh on growth.
Financial stability risks have risen along several
dimensions and the resilience of the global financial
system may be tested. A sudden repricing of risk from
an intensification of the war may expose, and interact
with, some of the vulnerabilities built up during the
pandemic, and lead to a sharp decline in asset prices.
Potential transmission channels of the war in Ukraine
on global financial markets include inflation pressure
from commodity price shocks, direct and indirect
exposures of banks and nonbank financial intermedi-
aries and firms, disruptions in commodity markets,
counterparty risk exposures, poor market liquidity
and funding strains, and cyberattacks affecting the
resilience of financial market utilities and broader
market functioning. While the financial system has
proven resilient to recent shocks, future shocks could
be more harmful.
Emerging and frontier markets are facing tighter
external financial conditions on the back of mon-
etary policy normalization and heightened geopoliti-
cal uncertainty, which is increasing downside risks
for portfolio flows. Emerging market sovereigns
have become more reliant on domestic banks for
funding, and bank holdings of domestic sovereign
debt have surged to historic highs. Distress in
emerging markets could trigger an adverse feed-
back loop between sovereigns and banks through
multiple channels—the sovereign-bank nexus—
potentially reducing bank soundness and lending
to the economy. In China, the ongoing stress in the
real estate sector and the increase in COVID cases
has raised concerns about a growth slowdown, with
potential feedback effects and possible spillovers to
other emerging markets.
Policymakers will need to confront these chal-
lenges by taking decisive actions to address finan-
cial vulnerabilities and rein in rising inflation. To
manage the delicate balance between containing
inflation and supporting the recovery from the pan-
demic, interest rates might have to rise beyond what
is currently priced in markets to get inflation back
to target in a timely manner. For many countries,
this may entail pushing interest rates well above
their neutral level.
While taking relevant steps to address energy
security concerns, policymakers should intensify
their efforts to implement the COP26 roadmap.
Although notable progress has been made to
strengthen the climate information architecture
in terms of disclosure standards and bridging data
gaps, focused policies aimed at scaling up private
finance in the transition to a greener economy
remain a major imperative.
The war in Ukraine has also brought to the
fore a number of medium-term structural issues
policymakers will need to confront in coming years.
The geopolitics of energy security may put climate

G LO B A L F I N A N C I A L S TA B I L I T Y R E P O R T: S H O C K WAV E S F R O M T H E WA R I N U K R A I N E T E S T T H E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
x International Monetary Fund | April 2022
transition at risk. Capital markets might become
more fragmented, with possible implications for
the role of the US dollar. And the fragmentation of
payment systems could be associated with the rise
of central bank digital currency blocs. In addition,
more widespread use of crypto assets in emerging
markets could undermine domestic policy objectives.
Multilateral cooperation will remain key to overcome
these medium-term challenges.
Tobias Adrian
Financial Counsellor

International Monetary Fund | April 2022 xi
EXECUTIVE SUMMARY
G
lobal financial conditions have tightened nota-
bly and downside risks to the economic outlook
have increased as a result of the war in Ukraine
(Figure 1). The tightening has been particularly
pronounced in eastern Europe and Middle East countries with
close ties to Russia, reflecting lower equity valuations and
higher funding costs. This has occurred just as most of the
world was slowly bringing the pandemic under control and the
global economy was recovering from COVID-19.
Financial stability risks have risen on several fronts, even
though so far, no global systemic event affecting financial
institutions or markets has materialized. A sudden repricing of
risk resulting from an intensification of the war and associated
escalation of sanctions may expose, and interact with, some of
the vulnerabilities built up during the pandemic, leading to a
sharp decline in asset prices.
With the sharp rise in commodity prices anticipated to
add to preexisting inflation pressure, central banks are faced
with a challenging trade-off between fighting record-high
inflation and safeguarding the post-pandemic recovery at a
time of heightened uncertainty about prospects for the global
economy (Figure 2). Bringing inflation back down to target
and preventing an unmooring of inflation expectations require
a delicate act in removing accommodation while preventing a
disorderly tightening of financial conditions that could interact
with financial vulnerabilities and weigh on growth. Incoming
inflation data suggest that more decisive tightening of mon-
etary policy is necessary in many countries
After rising early in the year on concerns about the inflation
outlook, advanced economy nominal bond yields have increased
further since the invasion, amid heightened volatility of rates
(Figure 3). Inflation break-evens (a market-implied proxy for
future inflation) have risen significantly on the back of sharply
higher commodity prices.
Repercussions of the Russian invasion of Ukraine and ensu-
ing sanctions continue to reverberate globally and will test
the resilience of the financial system through various potential
amplification channels, including direct and indirect exposures
of banks and nonbanks; market disruptions in commodity
markets and increased counterparty risk; poor market liquidity
and funding strains; acceleration of cryptoization in emerging
markets; and possible cyber-related events.
The war has already had an impact on financial interme-
diaries, nonfinancial firms, and markets directly or indirectly
exposed to Russia and Ukraine. Europe bears a higher risk than
other regions due to its proximity, reliance on Russia for energy
United States
Euro area
China
Europe, Middle East, and Africa
excluding Russia and Ukraine
Figure 1. Financial Conditions in Selected Regions
(Standard deviations from the mean)
–3
–2
–1
0
1
2
3
4
5
6
October
2021
GFSR
Tightening
2006 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22
Source: IMF staff calculations.
Note: GFSR = Global Financial Stability Report.
Figure 2. Near-Term Growth Forecast Densities
(Probability density)
Sources: Bloomberg Finance L.P.; and IMF staff calculations.
0.00
0.10
0.05
0.15
0.20
0.25
0.30
0.35
0.40
0.45
–4 –2 0 42
Global growth rate (percent)
6 8
Density for
year 2022:
at 2021:Q3
Unconditional density
Fifth percentiles
Density for
year 2022:
at 2022:Q1
Change in real yields Change in breakevens
Change in nominal yields
Figure 3. Year-to-Date Change in Yields
(Percentage points)
–0.5
2.0
0.0
0.5
1.0
1.5
US Euro area AustraliaUK Japan Canada
10
y
ea
r
5
ye
ar
5y
r5
yr
10
y
ea
r
5
ye
ar
5y
r5
yr
10
y
ea
r
5
ye
ar
5y
r5
yr
10
y
ea
r
5
ye
ar
5y
r5
yr
10
y
ea
r
5
ye
ar
5y
r5
yr
10
y
ea
r
5
ye
ar
5y
r5
yr
Sources: Bloomberg Finance L.P.; and IMF staff calculations.
Note: 5yr5yr (5-year, 5-year forward) corresponds to a five-year period that begins
five years from the current date.

G LO B A L F I N A N C I A L S TA B I L I T Y R E P O R T: S H O C K WAV E S F R O M T H E WA R I N U K R A I N E T E S T T H E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
xii International Monetary Fund | April 2022
needs, and the non-negligible exposure of some banks and other
financial institutions to Russian financial assets and markets.
Banks’ direct exposures to Russia are relatively small except
for some non-systemic European banks (Figure 4). Banks’
indirect exposures are more difficult to identify and assess
because they are less well known (especially the extent of
interconnectedness) as it is difficult to quantify them in the
absence of detailed and consistent disclosures by country or by
specific activity types. The risk is that indirect exposures could
be meaningful and surprise investors once revealed, leading to
a sharp rise in counterparty risk and risk premia. Foreign non-
bank financial intermediaries (NBFIs) have sizable investments
in Russian assets, with US and European investment funds
accounting for most of the exposures. As a share of total assets,
however, their exposure to Russia is small.
Dedicated emerging market funds have maintained a cautious
stance on their exposures to Russian debt since the Crimea
occupation in 2014, reducing their share of Russian debt from
more than 10 percent before 2014 to just over 4 percent in
2022. Funds benchmarked to global indices have had a much
smaller exposure to Russia, with an average 0.2 percent of their
assets invested in Russian debt in 2022.
Severe disruptions in commodity markets and supply chains
across the globe have caused extreme volatility in commodity
prices, amplified by pressures in commodity trade finance and
derivatives markets (Figure 5). Dealer banks play a crucial role
and have significant exposures in these markets, including by
providing liquidity and credit to a small group of large energy
trading firms that operate globally, are largely unregulated, and
are mostly privately owned. Pressures in commodity markets,
often magnified by poor liquidity, have led to lower risk appetite
and rising counterparty risk concerns, with implications for
funding conditions.
Emerging and frontier markets are facing tighter financial
conditions and higher risks of capital outflows. Since the
war in Ukraine began, emerging market (EM) hard currency
yields have increased at a rapid pace, akin to earlier episodes of
emerging market stress, before retracing some in mid-March
(Figure 6). The number of issuers trading at distressed levels
has surged to nearly 25 percent of issuers (Figure 7), surpassing
pandemic-peak levels. The deterioration in spreads, combined
with the increase in US yields, has pushed financing costs well
above their pre-pandemic levels for many borrowers. Markets
remain open for issuance at those higher levels of funding costs.
Flows in local currency bonds and equities have come under
pressure, experiencing the largest weekly redemptions since
March 2020. Tighter external financial conditions on the back
of US monetary policy normalization and heightened geopo-
litical uncertainty are likely to increase the downside risks for
portfolio flows (Figure 8).
International claims: Russia
Local claims: Russia
Total: Russia
International claims: Ukraine
Local claims: Ukraine
Total: Ukraine
Figure 4. Foreign Banks’ Gross Claims on Russia and Ukraine
(Billions of US dollars)
Sources: Bank for International Settlements Consolidated Banking Statistics; and
IMF staff calculations.
Note: Data labels use International Organization for Standardization (ISO) country
codes.
0
140
20
40
60
80
100
120
0
35
5
10
15
20
25
30
Total FRA ITA AUT USA JPN DEU NLD CHE GBR KOR
Right scale
Left scale
Weekly percent change
Figure 5. Commodity Price Changes, 1962–2022
(Percent)
–15
15
–10
–5
0
5
10
Sources: Bloomberg Finance L.P.; and IMF staff calculations.
Ja
n.
1
96
2
Ja
n.
6
6
Ja
n.
7
0
Ja
n.
7
4
Ja
n.
7
8
Ja
n.
8
2
Ja
n.
8
6
Ja
n.
9
0
Ja
n.
9
4
Ja
n.
9
8
Ja
n.
2
00
2
Ja
n.
0
6
Ja
n.
1
0
Ja
n.
1
4
Ja
n.
1
8
Ja
n.
2
2
Median EM
75th percentile of EM index
High yield (sub-investment grade)
Figure 6. Emerging Market Hard Currency Yields
(Percent)
2.5
12.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
10.5
11.5
Jan.
2016
July
16
Jan.
17
July
17
Jan.
18
July
18
Jan.
19
July
19
Jan.
20
July
20
Jan.
21
July
21
Jan.
22
Sources: Bloomberg Finance L.P.; and IMF staff estimates.
Note: EM = emerging market; HY = high-yield. Yields based on JPMorgan
Emerging Market Bond Index.

e X e C U t I V e s U M M A R Y
International Monetary Fund | April 2022 xiii
In China, the recent equity sell-off, particularly in the tech
sector, and the increase in COVID-19 cases have raised concerns
about a growth slowdown, with possible spillovers to emerg-
ing markets. Ongoing stress in the battered real estate sector has
increased financial stability risks and added to growth pressures.
Extraordinary financial support measures may be necessary to ease
pandemic-driven balance sheet pressures but would add further to
medium-term debt vulnerabilities.
The interlinkages between emerging market sovereigns and
domestic banks have intensified over the past two years as
additional government financing needs to cushion the impact of
the pandemic have been mostly met by banks (see Chapter 2).
As a result, bank holdings of domestic sovereign debt surged to
historic highs in 2021 (Figure 9). Distress in emerging markets
could trigger an adverse feedback loop between sovereigns and
banks through multiple channels—the so-called sovereign-bank
nexus—potentially reducing bank soundness and lending to the
economy.
The war in Ukraine has brought to the fore a number of
medium-term structural issues policymakers will need to con-
front in coming years, including the possibility that the geopoli-
tics of energy security may put climate transition at risk; the risk
of fragmentation of capital markets and possible implications for
the role of the US dollar; the risk of fragmentation in payment
systems and the creation of blocs of central bank digital curren-
cies; more widespread use of crypto assets in emerging markets;
and more complex and bespoke asset allocations in an effort to
preempt the possible imposition of sanctions.
The war has made evident the urgency to cut dependency
on carbon-intensive energy and to accelerate the transition to
renewables. However, in the face of growing concerns about
energy security and access to energy sources (Figure 10), the
energy transition strategy may face setbacks for some time. The
current energy crisis may alter the speed of phasing out fossil
fuel subsidies in emerging market and developing economies,
while rising inflation pressure may also lead authorities to
resort to subsidies or other forms of fiscal support to households
or firms.
Crypto asset trading volumes against some emerging market
currencies have spiked following the introduction of sanctions
against Russia and the use of capital restrictions in Russia and
Ukraine. This is occurring against a longer-term increase in such
cross-border transactions, bringing to the fore the challenges of
applying capital flow measures and sanctions.
While technological innovation in financial activities (fintech)
can support inclusive growth by strengthening competition,
financial development, and inclusion (Chapter 3), the rapid
growth of risky business segments can be a cause of concern for
financial stability when fintech firms (fintechs) are subject to less
stringent regulation (Figure 11).
Figure 9. Bank-Sovereign Debt Exposure, 2005–21
(Percent)
0
20
2
4
6
8
10
12
14
18
16
Sources: IMF, Monetary and Financial Statistics; and IMF staff calculations.
Note: See Figure 2.1, panel 2 of Chapter 2 for more information. AEs = advanced
economies; EMs = emerging markets.
2005–09 2010–14 2015–19 2020 2021
Pe
rc
en
t o
f b
an
ki
ng
s
ec
to
r
as
se
ts
AEs EMs
Share Distressed EMs (spread >1,000 bps)
Figure 7. Distressed Sovereign Hard Currency Issuers
(Number of sovereigns with spreads above 1,000 basis points;
share of total)
0
40
5
10
15
20
25
30
35
2006 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22
Sources: JPMorgan Chase & Co.; and IMF staff calculations.
Note: bps = basis points; EMs = emerging markets.
Hard currency bonds
Equities (China)
Local currency bonds
Equities (EMs excluding China)
EMs excluding Chinese equities
Figure 8. Fund Flows to Emerging Markets
(Billions of US dollars, two-week moving sum)
–10
20
–5
0
5
10
15
Ja
n.
2
02
1
Fe
b.
2
1
M
ar
. 2
1
Ap
r.
2
1
M
ay
2
1
Ju
ne
2
1
Ju
ly
2
1
Au
g.
2
1
Se
p.
2
1
O
ct
. 2
1
N
ov
. 2
1
D
ec
. 2
1
Ja
n.
2
2
M
ar
. 2
2
Fe
b.
2
2
Sources: EPFR; and IMF staff calculations.
Note: EMs = emerging markets.

G LO B A L F I N A N C I A L S TA B I L I T Y R E P O R T: S H O C K WAV E S F R O M T H E WA R I N U K R A I N E T E S T T H E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
xiv International Monetary Fund | April 2022
Policy Recommendations
Central banks should act decisively to prevent inflation
pressure from becoming entrenched and avoid an unmoor-
ing of inflation expectations. To avoid unnecessary volatility in
financial markets, it is crucial that central banks in advanced
economies provide clear guidance about the normalization pro-
cess while remaining data dependent.
Emerging markets remain vulnerable to a disorderly tighten-
ing of global financial conditions. Many central banks have
already significantly tightened policy. Further rate increases,
or policy normalization with respect to other measures taken
during the pandemic (such as asset purchases), should con-
tinue as warranted according to the country-specific inflation
and economic outlook to anchor inflation expectations and
preserve policy credibility.
Policymakers should tighten selected macroprudential tools
to tackle pockets of elevated vulnerabilities while avoiding a
disorderly tightening of financial conditions. Striking a balance
between containing the buildup of vulnerabilities and avoiding
procyclicality appears important given uncertainties about the
economic outlook, the ongoing monetary policy normaliza-
tion process, and limits on fiscal space in the aftermath of
the pandemic.
While taking steps to address energy security concerns,
policymakers should intensify their efforts to implement the
2021 United Nations Climate Change Conference (COP26) road
map to achieve net-zero targets. They should take measures to
increase the availability and lower the cost of fossil fuel alterna-
tives and renewables while improving energy efficiency; scale up
private finance in the transition to a greener economy; and con-
tinue to strengthen the climate finance information architecture.
Policymakers should develop comprehensive global standards
for crypto assets along the activity and risk spectrum. A more
robust oversight of fintech firms and decentralized finance (DeFi)
platforms is needed to take advantage of their benefits while
mitigating their risks. To preserve the effectiveness of capital flow
management measures in an environment of growing usage of
crypto assets, policymakers need to pursue a multifaceted policy
strategy. Recent measures taken in markets and exchanges in
response to elevated volatility in commodity prices highlight the
need for regulators to examine the broader implications, including
exchange governance mechanisms, resiliency of trading systems,
concentration of risk, margin setting, and trading transparency in
exchange and over-the-counter markets.
Share in production
Price change between February 23 and March 23, 2022 (right scale)
Figure 10. Russia’s Share in Global Production
(Percent)
0
18
2
4
6
8
10
12
14
16
–10
–5
0
5
10
15
20
25
30
35
40
Aluminum Copper Nickel CoalPlatinum Oil Gas
Sources: US Geological Survey, National Minerals Information Center; and IMF
staff calculations.
Stablecoins (others, left scale)
Stablecoins (USDC, left scale)
Stablecoins (USDT, left scale)
Stablecoins total (left scale)
DeFi total (right scale)
Figure 11. Value of DeFi Assets and Stablecoins
(Billions of US dollars)
0
180
20
40
60
80
100
120
140
160
0
120
20
40
60
80
100
Sources: CoinGecko; DeFi Pulse; and IMF staff calculations.
Note: DeFi = decentralized finance; USDC = USD Coin; USDT = USD Tether.
Ja
n.
2
02
0
M
ar
. 2
0
M
ay
2
0
Ju
ly
2
0
Se
p.
2
0
N
ov
. 2
0
Ja
n.
2
1
M
ar
. 2
1
M
ay
2
1
Ju
ly
2
1
Se
p.
2
1
N
ov
. 2
1
Ja
n.
2
2

International Monetary Fund | April 2022 xv
IMF EXECUTIVE BOARD DISCUSSION OF THE OUTLOOK,
APRIL 2022
E
xecutive Directors broadly agreed with staff’s
assessment of the global economic outlook,
risks, and policy priorities. They noted
that the war in Ukraine has led to a costly
humanitarian crisis, with economic and financial
repercussions and spillovers—through commodity mar-
kets, confidence, trade, and financial channels—that
have prompted a downgrade to the global economic
outlook and increased inflationary pressures at a time
when the global economy has not yet recovered from
the COVID-19 crisis. Directors concurred that the
sharp increase in uncertainty could make economic
projections especially volatile. They agreed that emerg-
ing risks—from an intensification of the war, further
sanctions on Russia, fragmentation in financial and
trade markets, and a sharper-than-expected slowdown
in China due to COVID-19 outbreaks—on top of
the continued risk of new, more virulent COVID-19
strains have further tilted the balance of risks to the
downside. Moreover, Directors noted that the war in
Ukraine has increased the likelihood of food short-
ages and wider social tensions given higher food and
energy prices, which would further adversely impact
the outlook.
Against this backdrop, Directors agreed that policy
priorities differ across countries, reflecting local
circumstances and differences in trade and financial
exposures. Directors emphasized that the layering of
strains—slowing economic growth, persistent and
rising inflation pressures, increased food and energy
insecurity, continued supply chain disruptions, and
COVID-19 flare-ups—further complicates national
policy choices, particularly for countries where policy
space shrank after the necessary response to the
COVID-19 pandemic. At the global level, Directors
stressed that multilateral cooperation and dialogue
remain essential to defuse geopolitical tensions and
avoid fragmentation, end the pandemic, and respond
to the myriad challenges facing our interconnected
world, particularly climate change.
Directors concurred that, in many countries, fiscal
policy is operating in a highly uncertain environ-
ment of elevated inflation, slowdown in growth, high
debt, and tightening borrowing conditions. While
acknowledging that fiscal policy has a role to play in
moments of large adverse shocks, Directors considered
that, particularly for countries with tighter budget
constraints, fiscal support should focus on priority
areas and target the most vulnerable. They emphasized
that, in countries where economic growth is strong and
where inflation is elevated, fiscal policy should phase
out pandemic-related exceptional support, moving
toward normalization. Directors acknowledged that
many emerging markets and low-income countries face
difficult choices given limited fiscal space and higher
demands on governments due to energy disruptions
and the pressing need to ensure food security. In this
context, they underscored that a sound and credible
medium-term fiscal framework, including spending
prioritization and measures to raise revenues, can help
manage urgent needs while ensuring debt sustain-
ability. Directors stressed that short-term measures
to mitigate high food and energy prices should not
undermine actions to ensure greater resilience through
investment in health, food, and cleaner energy sources.
Directors concurred that monetary authorities
should act decisively to prevent inflationary pressures
from becoming entrenched and avoid a de-anchoring
of inflation expectations. They noted that central banks
in many advanced and emerging market economies
need to continue tightening the monetary policy stance
to bring inflation credibly back to target and preserve
hard-built policy credibility. Directors stressed that
transparent, data-driven, and clearly communicated
monetary policy is critical to avoid financial insta-
bility. They considered that, should global financial
The following remarks were made by the Chair at the conclusion of the Executive Board’s discussion of the
Fiscal Monitor, Global Financial Stability Report, and World Economic Outlook on April 11, 2022.

G LO B A L F I N A N C I A L S TA B I L I T Y R E P O R T: S H O C K WAV E S F R O M T H E WA R I N U K R A I N E T E S T T H E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
xvi International Monetary Fund | April 2022
conditions tighten suddenly, emerging and developing
economies could face capital outflows and should
be ready to use all available tools, including foreign
exchange interventions and capital flow management
measures, when needed and in line with the Fund’s
Institutional View on the Liberalization and Manage-
ment of Capital Flows and without substituting for
exchange rate flexibility and warranted macroeconomic
adjustments.
Directors agreed that the war in Ukraine will test
the resiliency of the financial system. They noted that,
although no systemic event has materialized so far,
financial stability risks have risen along many dimen-
sions while global financial conditions have tightened
significantly. Directors concurred that, in those emerg-
ing markets where the sovereign-bank nexus could pose
vulnerabilities, it should be closely monitored. They
also noted risks of fragmentation of capital markets
and payment systems, the creation of blocks of central
bank digital currencies, a more widespread use of
crypto assets, and more frequent cyberattacks. Direc-
tors recommended tightening selected macroprudential
tools to tackle pockets of elevated vulnerabilities while
avoiding procyclicality and a disorderly tightening of
financial conditions. They also called for comprehen-
sive global standards and a multifaceted strategy for
crypto assets and for a more robust oversight of fintech
firms and decentralized finance platforms.
Directors agreed that strong multilateral coopera-
tion is essential to respond to existing and unfold-
ing humanitarian crises, safeguard global liquidity,
manage debt distress, ensure food security, mitigate
and adapt to climate change, and end the pandemic.
Noting that many countries are coping with higher
volatility, increased spending from the pandemic and
humanitarian crises, and tightening financial condi-
tions, Directors called on the Fund and other multi-
lateral institutions to stand ready to provide financial
support. At the same time, they noted that prompt
and orderly debt restructuring, particularly by improv-
ing the G20 Common Framework, will be necessary in
cases where liquidity support is insufficient. Directors
noted that increasingly dire climate change develop-
ments heighten the urgency for tangibly advancing
the green economic transformation. They stressed the
importance of intensifying efforts to implement the
COP26 roadmap together with appropriate measures
to address energy security concerns. Directors con-
sidered that international cooperation in corporate
taxation and carbon pricing could also help mobilize
resources to promote the necessary investments and
reduce inequality. As the pandemic persists, Directors
underscored that prompt, equitable, and wider access
to vaccinations, testing, and treatments remains a key
priority. They also reiterated that measures to address
the scars from the pandemic remain crucial to boost
long-term prospects and create a more resilient and
inclusive global economy. Above all, Directors called
for a peaceful resolution of the war in Ukraine, an end
to the resulting humanitarian crisis, and a return to the
rules-based international order that helped lift millions
out of poverty over the past decades.

The War in Ukraine Raises Immediate Financial
Stability Risks and Questions about the
Longer-Term Impact on Markets
Early in the year, financial markets were squarely
focused on rising risks to the inflation outlook and
implications for the global economy, especially
given concerns about a possible slowdown in China.
Investors were worried that central banks in advanced
economies would have to normalize policy more
aggressively than anticipated only a few months earlier,
causing a sharp tightening in financial conditions,
especially in emerging markets. The war in Ukraine,
while at this point not a global systemic event from
a financial standpoint, is nonetheless anticipated to
have a material impact on the economy amid height-
ened uncertainty about the outlook. In addition, the
sharp rise in commodity prices further complicates the
challenge faced by central banks in credibly bring-
ing down inflation to target while safeguarding the
post-pandemic recovery.
Chapter 1 at a Glance
• Global financial conditions have tightened notably and downside risks to the economic outlook have
increased as a result of the Russian invasion of Ukraine. This has occurred in the context of the pandemic,
which was slowly being brought under control, and the consequent recovery of the global economy from
COVID-19.
• Financial stability risks have risen along many dimensions, although no global systemic event affecting
financial institutions or markets has materialized so far.
• The sharp rise in commodity prices, which has exacerbated preexisting inflation pressure, poses
challenging trade-offs for central banks.
• Repercussions of the war continue to reverberate globally and will test the resiliency of the financial system
through various channels, including direct and indirect exposures of banks, nonbank financial intermediaries,
and firms; market disruptions (including in commodity markets) and increased counterparty risk; acceleration
of cryptoization in emerging markets; and possible cyber-related events.
• Emerging and frontier markets are facing tighter financial conditions and a higher probability of portfolio
outflows (forecast at 30 percent now, up from 20 percent in the October 2021 Global Financial Stability
Report [GFSR]).
• In China, financial vulnerabilities remain elevated amid ongoing stress in the property development sector
and new COVID-19 outbreaks.
• In coming years, policymakers will need to confront a number of structural issues brought to the fore by the
war in Ukraine and the associated sanctions against Russia, including the trade-off between energy security
and climate transition, market fragmentation risks, and the role of the US dollar in asset allocation.
• Energy and food security concerns are acute and may put climate transition efforts at risk.
• Policymakers need to take decisive actions to rein in rising inflation and address financial vulnerabilities
while avoiding a disorderly tightening of financial conditions that would jeopardize the post-pandemic
economic recovery. Some businesses and households may need short-term fiscal support to navigate the
consequences of the war.
• The surge in volatility and dislocations in commodity markets underscores the importance of ensuring the
adequacy of disclosures and standards of transparency to counterparties, especially major financial institutions.
This is essential to support comprehensive risk management and supervisory oversight.
THE FINANCIAL STABILIT Y IMPLICATIONS OF THE WAR IN UKRAINE1CHAPTE
R
International Monetary Fund | April 2022 1

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
2 International Monetary Fund | April 2022
The repercussions of the Russian invasion of Ukraine
in terms of economic damage will be greater for the
war region and Europe. In particular, official sanctions1
and further escalations thereof, multiple companies
voluntarily severing ties with Russia, together with
steps taken by several countries to wean off Russian
energy imports, will cause substantial damage to the
Russian economy. But the war is also expected to have
significant implications for the global economy (see the
April 2022 World Economic Outlook [WEO]) and for
global financial markets beyond immediate financial
stability risks. The severity of the disruptions in com-
modity markets and to global supply chains will weigh
heavily on the outlook for inflation, the global econ-
omy, and possibly macro-financial stability. In addition,
record high food prices could have implications for
social unrest in some emerging and frontier markets.2
War is a risk that is difficult to insure against or
hedge, so it is only natural that investors precipi-
tously pull back from risk taking, causing volatility
and correlations across asset classes to rise. Eventually,
however, asset prices tentatively stabilized around a
new normal as market participants assess the evolution
of the war, geopolitical implications, and prospects for
different asset classes and the economy.
The information content and signal that can be
extracted from price moves of Russian and Ukrainian
assets are severely limited by the sanctions and lack
of liquidity in these markets. That said, such assets
have experienced the largest price declines, with
dollar-denominated sovereign bonds pricing a very
high probability of default and a low rate of recovery
(Figure 1.1, panel 1). The Russian ruble has fallen
to all-time low levels against the US dollar, before
recovering a substantial portion of the earlier declines.
1Several advanced economies, including the United States, mem-
bers of the European Union, Japan, and the United Kingdom, have
imposed an unprecedented range of sanctions on Russia. These have
prohibited financial institutions from engaging in any transaction
involving the Central Bank of Russia, thus hindering its ability to
access a substantial portion of its foreign reserves. Other sanctions
have effectively banned all major Russian banks not related to the
energy sector from doing business in the United States, the European
Union, the United Kingdom, and Japan and have frozen their assets,
while some large banks have also been banned from the SWIFT sys-
tem. In addition, some entities and individuals have faced sanctions,
and trade restrictions have been put in place on a variety of goods.
Finally, some jurisdictions have announced bans on energy imports
from Russia or plans to reduce their dependence on Russian energy.
2The United Nations food price index has already surpassed the
levels seen in 2011, when social unrest was triggered in the Middle
East and North Africa region.
The Ukrainian hryvnia exchange rate has been fixed as
of February 24 (Figure 1.1, panel 2). Stock trading on
the Moscow Exchange was halted on February 25 and
reopened only on March 24 with substantial restric-
tions on trading (Figure 1.1, panel 3).
Among huge uncertainties and shifting prospects on
the ground, investors have focused on severe disrup-
tions in commodity markets as a crucial transmission
channel and amplifier of the crisis. Disruptions could
intensify in the event of a further escalation of the
sanctions that could include an explicit ban of energy
imports from Russia by Europe. Energy and food
prices have risen sharply, and volatility has jumped
(Figure 1.2, panels 1 and 2).
The rise in agricultural prices has important spillover
effects for developing economies and emerging markets—
especially in eastern Europe, the Caucasus, the Middle
East, and North Africa—that are close trading partners
of Russia and Ukraine. Metals, another Russian com-
modity export, is also affected, which has strong impli-
cations for global supply chains, including the renewable
energy industry (Figure 1.2, panel 3; see also Box 1.1 for
recent developments on nickel trading and the WEO
Special Feature on commodities). Supply shortages are
expected to persist, as seen in the very high relative price
of short-term contracts over longer-term ones (Fig-
ure 1.2, panel 4).
After an initial deterioration of risk appetite following
the Russian invasion of Ukraine, investors have become
more optimistic about the outlook for risk assets since
mid-March, with global equities recouping most of the
earlier losses. Sectors already adversely affected by the
pandemic—the airline and hospitality sectors—have seen
large declines in stock prices (Figure 1.3, panel 1, upper
segment). Other energy-intensive and energy-dependent
sectors, such as automobiles, consumer durables, and
industrials, have been hit by surging energy and metal
prices, exacerbating COVID-19–related supply chain
challenges. The food industry has come under pressure
from the sharp rise in energy and agricultural commodity
prices. Finally, Russia and Ukraine produce some critical
inputs—gases and precious metals—for the information
technology sector, particularly semiconductors, adding to
supply chain challenges.3 As a result, there are growing
concerns about further chip shortages and the associ-
ated impact on supply chains, delaying the resolution of
pandemic-related issues and further inflating prices.
3See Chris Nuttall, “Ukraine War Is Chip Industry’s Kryptonite,”
Financial Times (March 4, 2022).

C H A P T E R 1 T h E F I N A N C I A L S T A B I L I T Y I M P L I C A T I O N S O F T h E w A R I N U k R A I N E
3International Monetary Fund | April 2022
Across regions, equity prices have been less affected
in the United States and advanced Asia, as these
economies are seen as relatively more shielded from the
direct impact of the war and supported by the strong
incoming economic data. In Europe, by contrast,
investors appear to be more concerned about possible
risks to the economic and inflation outlook given their
geographical proximity to the war, relatively larger
exposures, and energy dependency on Russia. Equity
prices have fallen in emerging markets, in sync with
rising external financing costs. The impact has been
particularly pronounced for economies in central and
eastern Europe. Chinese equities’ notable underperfor-
mance in this period reflected rising geopolitical risks
but also domestic factors like growth concerns amid
COVID-related lockdowns and regulatory uncertainty
in the tech industry.
Global corporate bond spreads have widened
some, surpassing pre-pandemic levels across major
sectors and most high-yield segments (Figure 1.3,
panel 2). The increase has been more evident for
the lowest-rated firms, pointing to concerns about
potential future defaults. In emerging markets, inves-
tors appear to be differentiating across countries, with
those with closer economic ties to Russia through trade
and remittances (Caucasus and Central Asia) and more
risk-sensitive frontier market economies hit the hardest
(Figure 1.3, panel 3). Currencies of Latin American
countries and commodity exporters have outperformed
relative to eastern European countries and oil import-
ers in Asia (Figure 1.3, panel 4).
Volatility has risen sharply in both equity and
interest rate markets following the Russian invasion
of Ukraine, reflecting heightened uncertainty on the
economic and policy outlook (Figure 1.4, panels 1
and 2). In equities, market-implied volatility has
declined sharply recently, in some cases to levels below
those that prevailed before the war, and is anticipated
to remain around these levels through the end of 2022.
In interest rates, market-implied volatility has remained
elevated, reflecting uncertainties about the policy nor-
malization process in advanced economies.
On balance, financial conditions in advanced econ-
omies have tightened notably this year, reflecting the
UKR spread
RUS spread
Sberbank
(London)
Gazprom
(London)
Russia (MOEX)
VanEck Russia
ETF
RUB per USD
UAH per USD
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Jan.
2022
Feb.
22
Mar.
22
Apr.
22
Jan.
2022
Feb.
22
Mar.
22
Apr.
22
Jan.
2022
Feb.
22
Mar.
22
Apr.
2219
98
20
00 03 06 09 12 15 18 21
0
20
40
60
80
100
120
90
100
110
120
130
140
150
160
170
180
190
1. Sovereign Credit Spreads
(Basis points)
2. Russia and Ukraine Exchange Rates
(Indexed to Jan. 1, 2022)
3. Russian Equities
(Indexed to Jan. 1, 2022)
Russian and Ukrainian bonds are pricing a high
probability of default amid poor liquidity for credit
instruments.
The ruble hit record lows before retracing
most of its losses.
Russian equities listed abroad collapsed,
and the domestic market was closed for
a month before reopening in late March.
Sources: Bloomberg Finance L.P.; and IMF staff calculations.
Note: The Ukrainian hryvnia exchange rate has been effectively fixed since February 24, 2022, with only limited trading in parallel markets. The Moscow Stock
Exchange (MOEX) was closed from February 28–March 24. In panel 1, UKR refers to the United Kingdom–Russia spread; RUS spread refers to the Russia–United
States spread. ETF = exchange-traded fund; RUB = Russian ruble; UAH = Ukrainian hryvnia.
Figure 1.1. Russian and Ukrainian Assets Have Come under Heavy Pressure Following the War in Ukraine

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
4 International Monetary Fund | April 2022
decline in corporate valuations, higher government bond
yields, and continued expectations of monetary policy
normalization. However, relative to historical levels,
financial conditions remain easy or roughly neutral (Fig-
ure 1.5, panel 1). The sudden and significant increase in
external borrowing costs and rising local currency rates
have weighed heavily on financial conditions in eastern
Europe and the Middle East with close ties to Russia
(Figure 1.5, panel 2). Conditions have also tightened
for many other emerging market economies, reflecting
higher interest rates to combat inflation, lower equity
valuations, and higher external borrowing costs. By
contrast, conditions have eased in China, as policymak-
ers have provided additional policy support to offset an
economic slowdown, partly stemming from continued
strains among property developers.
The Russian invasion of Ukraine is anticipated to
have a material impact on the post-pandemic global
economic recovery. Global economic growth for 2022
has been marked down to 3.6 percent, 0.8 percentage
point lower than projected in the January 2022 WEO
Update (see the April 2022 WEO). Amid heightened
uncertainty, the balance of risks to growth this year
remains skewed to the downside, as demonstrated via
the growth-at-risk framework (Figure 1.6, panel 1).4
Moreover, the probability of growth falling below zero
in 2022 is estimated at about 8 percent, with downside
risks now at elevated levels compared with historical
norms (Figure 1.6, panel 2).
4See Chapter 3 of the October 2017 GFSR for details of the
Growth-at-Risk model.
Oil Wheat Natural gas (right scale) WTI-implied volatility Brent-Urals oil price gap
(right scale)
Aluminum Metals Nickel
Figure 1.2. Impact of the War in Ukraine on Commodities
Several commodity prices have risen dramatically on fears of supply
disruptions …
1. Energy and Agricultural Commodity Prices
(Index, January 1, 2022 = 100)
… and volatility in financial markets has spiked.
2. Oil Implied Volatility and Brent-Urals Price Spread
(Percent; US dollars per barrel)
0
10
20
30
Industrial metals prices have surged amid risks to supply chains and
trading disruptions on exchanges.
3. Metals Prices
(Index, January 1, 2022 = 100)
Supply shortages are expected to persist in the short term for multiple
commodities.
4. Share of Commodities in Backwardation
(Percent; 1st to 6th contract spreads in 21 futures)
Sources: Bloomberg Finance L.P.; and IMF staff calculations. Panel 3 uses three-month futures from the London Metals Exchange and Bloomberg Metals Index.
Note: In panel 2, the volatility is three months annualized. In panel 4, backwardation occurs when the first contract price is higher than the prices of later contracts.
WTI = West Texas Intermediate crude oil futures.
85
100
115
130
145
160
175
190
Oct. 2021 Nov. 21 Dec. 21 Jan. 22 Feb. 22 Mar. 22
60
100
140
180
220
260
300
340
30
40
50
60
70
Sep. 2021 Nov. 21 Jan. 22 Mar. 22
Oct. 2021 Nov. 21 Dec. 21 Jan. 22 Feb. 22 Mar. 22 14 18 222010
80
130
180
230
280
0
20
40
60
80
100

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5International Monetary Fund | April 2022
Despite the anticipated economic impact, espe-
cially in the war region and Europe, no global sys-
temic event affecting financial institutions or markets
has materialized so far. This reflects, at least in part,
the increased resilience of the global financial system
resulting from the implementation of the financial
regulatory agenda following the global financial crisis.
However, financial stability risks have risen on several
fronts since the Russian invasion of Ukraine, and they
may test the resilience of global financial markets amid
huge uncertainties, especially should stress interact
with preexisting vulnerabilities (see Online Box 1.15
5Online Box 1.1. is at: www .imf .org/ en/ Publications/ GFSR.
on financial vulnerabilities). Inflation pressure related
to surging commodity prices has worsened the pol-
icy trade-off faced by central banks, raising concerns
among investors about the readiness of central banks to
backstop financial markets in the event of sharp declines
in asset prices. Moreover, a sudden repricing of risk
resulting from an intensification of the war, including
a widening of the war beyond Ukraine and Russia, and
an associated escalation of sanctions, may expose, and
interact with, some of the vulnerabilities that have built
up during the pandemic and lead to a sharp decline in
asset prices. For example, the recent equity sell-off in
China, particularly in the tech sector, combined with
ongoing stress in the real estate sector and the increase
January–late February War period Pre-pandemic Present
Jan. 1– Feb. 23 Since Feb. 23 2022 YTDNet change since Feb. 23 Max sell off
1. Global Equity Price Changes in 2022
(Percent)
2. Credit Spread Levels by Sector and Credit Rating
(Basis points)
3. Change in Emerging Market Sovereign Bond Spreads
(Basis points)
350
–50
0
50
100
150
200
250
300
Caucasus/
Central Asia
Frontiers EM All EM
excluding
RUS, UKR
POL/HUN/
ROU
LatAm
4. Emerging Market Currencies
(Percent appreciation/depreciation, vs. US dollar)
–15
20
–10
–5
0
5
10
15
Energy
Airlines
Food and staples retail
Financials
Health care
IT
Communication
Consumer discretionary
Japan
US
EMs excluding China
Europe
China
–20 –15 –10 –5 0 5 10 15 20 25 0 500 1,000 1,500 2,000 2,500 3,000
0 50 100 150
BR
A
ZA
F
CO
L
CH
L
PE
R
M
EX
G
EO ID
N
M
YSPH
L
IN
D
H
UNRO
U
PO
L
KA
Z
TU
R
Ba
B
Caa
Ca–D
Sources: Bloomberg Finance L.P.; JPMorgan Chase & Co.; and IMF staff calculations.
Note: In panel 3, the Caucasus/Central Asia includes the average of Armenia, Azerbaijan, Georgia, Kazakhstan, and Tajikistan. In panels 3 and 4, data labels use
International Organization for Standardization (ISO) country codes. EM = emerging markets; IT = information technology; LatAm = Latin America; YTD = year to date.
Equities have sold off, on net, in emerging markets and sectors
affected by commodity prices and supply chain disruptions concerns …
… and credit spreads have widened the most in low-rated firms.
Weaker borrowers and Russia’s economic partners have been hit the
hardest, but spreads have recovered after the initial shock.
Currencies of Russia’s main trading partners have sold off, but
commodity exporters have held up.
Figure 1.3. Impact of the War in Ukraine on Financial Assets
Energy
Basic
Communications
Transportation
Consumer cyclical
Consumer noncyclical
Capital goods

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
6 International Monetary Fund | April 2022
US
Euro area
Japan
US
Euro area
Japan (right scale)
Figure 1.4. Financial Market Volatility Has Picked Up Dramatically
Market volatility has spiked following the war in Ukraine, especially in Europe, but it has fallen notably recently.
1. Equity Option Implied Volatility
(Percent, one-month, annualized)
10
15
20
25
30
35
40
45
Jan. 23Jan. 2021 July 21 Jan. 22 July 22
2. Interest Rate Volatility (Overnight Indexed Swaption 2-year × 2-year
Implied Volatility)
(Basis points, annualized)
10
15
20
25
30
35
40
0
20
40
60
80
100
120
140
Jan. 2021 Jan. 23July 21 Jan. 22 July 22
Sources: Bloomberg Finance L.P.; and IMF staff calculations. 
Note: In panel 1 and 2, dotted lines indicate forwards. In panel 2, OIS swaption (swap option) refers to an option to enter into an overnight index swap (OIS).
EUR = euro; JPY = Japanese yen; USD = US dollar.
United States
Euro area
Other advanced
economies
China
Asia excluding China
Latin America
Europe, Middle East, and Africa
excluding Russia and Ukraine
–2
–1
0
1
2
3
4
5
6
–3
–2
–1
0
1
2
3
22
20
06 07 09 11 13 15 17 1908 10 12 14 16 18 20 21 22
20
06 07 09 11 13 15 17 1908 10 12 14 16 18 20 21
October
2021
GFSR
October
2021
GFSR
Tightening Tightening
2. Financial Conditions: Emerging Markets
(Standard deviations from the mean)
Sources: Bloomberg Finance L.P.; Haver Analytics; national data sources; and IMF staff calculations.
Note: GFSR = Global Financial Stability Report.
Financial conditions have tightened notably on average in Q1 in
advanced economies, especially in the euro area …
… and have reached extremely tight levels in eastern Europe.
1. Financial Conditions: Advanced Economies
(Standard deviations from the mean)
Figure 1.5. Global Financial Conditions

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7International Monetary Fund | April 2022
in COVID-19 cases, has raised concerns about a
growth slowdown, with possible spillovers to emerging
markets. In addition, the war has crystallized specific
amplification channels of the shock that operate through
financial markets—for example through disruptions
in commodity markets and widespread counterparty
risk concerns that have propagated and weighed on
risk-taking appetite across market segments.
Potential transmission channels of the war in Ukraine
through global financial markets include inflation
pressure related to rising commodity prices; exposures
of banks and nonbank financial intermediaries to
Russian and Ukrainian assets; disruptions in commodity
markets transmitted through commodity trade finance
and derivatives; growing concerns about counterparty
risks leading to a broad pullback in risk-taking amid
poor market liquidity and funding strains; a Russian
default on its debt obligations and potential capital out-
flows from emerging markets; and cyberattacks affecting
the resilience of the financial system.
In coming years, policymakers will face a number
of structural challenges brought to the fore by the war
in Ukraine. These include a change in the perception
of the trade-offs between energy security and climate
transition at a time when higher commodity prices
and supply disruptions will likely make the transition
toward energy renewables more costly and complex;
de-globalization and fragmentation of capital mar-
kets as a result of recurring geopolitical events, with
possible long-term implications for the composition
of exchange rate reserves; the risk of fragmentation in
payment systems and the creation of central bank dig-
ital currency blocs; and more widespread use of crypto
assets in emerging markets to bypass capital restrictions
and sanctions. These issues are extremely complex in
a world where geopolitics is likely to play a major role
with respect to asset allocations and uncertainty reigns.
Implications of Higher Commodity Prices for
Monetary Policy
Central Bank Normalization in Advanced Economies:
Walking a Tightrope amid Stubbornly High Inflation
With higher commodity prices expected to add
to inflation pressure that has been accelerating since
the October GFSR, central banks are faced with a
Quintiles
Worst Best
1. Near-Term Growth Forecast Densities
(Probability density)
2. Near-Term Growth-at-Risk Forecasts
(Percentile rank)
The downward revision to global growth forecast for 2022 coincides
with the balance of risks remaining skewed to the downside.
Downside risks are now at elevated levels compared with historical
norms.
Sources: Bank for International Settlements; Bloomberg Finance L.P.; Haver Analytics; IMF, International Financial Statistics database; and IMF staff calculations.
Note: Forecast density estimates are centered around the World Economic Outlook forecasts for 2022 as at 2021:Q3 and 2022:Q1, respectively. To gauge downside
risks over time, in panel 2, the black line traces the evolution of the 5th percentile threshold (the growth-at-risk metric) of near-term growth forecast densities. The
color of the shading depicts the percentile rank for the growth-at-risk metric, from 1991 onward. See the April 2018 Global Financial Stability Report for details.
Figure 1.6. Global Growth-at-Risk
0
20
40
60
80
100
0.00
0.10
0.05
0.15
0.20
0.25
0.30
0.35
0.40
0.45
–4 –2 0 42
Global growth rate (percent)
6 8
Density for
year 2022:
at 2021:Q3
Unconditional density
Fifth percentiles
Density for
year 2022:
at 2022:Q1
20
08 09 10 11 12 13 14 15 16 17 18 19 20 21 22

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8 International Monetary Fund | April 2022
challenging trade-off between fighting multiyear-high
inflation and safeguarding the recovery at a time of
heightened uncertainty about prospects for the global
economy. Bringing inflation down to target and
preventing an unmooring of inflation expectations
require careful communication and actions to prevent
a disorderly tightening of financial conditions. Such a
tightening, especially if interacting with financial vul-
nerabilities, could pose risks to financial stability and
weigh on growth.
After rising early in the year on concerns about the
inflation outlook, advanced economy nominal bond
yields increased sharply in March amid heightened
interest rate volatility, reflecting an increase of both
breakevens and real rates (Figure 1.7, panel 1). The
yield increase accelerated in early April as investors
reassessed their outlook for monetary policy following
the formal commencement of the normalization pro-
cess by the Federal Reserve at its March Federal Open
Market Committee (FOMC) meeting.
US 5 year
EA 5 year
Expected inflation (risk-adjusted) Inflation risk premia Less than 1% Between 1–2% Between 2–3% Greater than 3%
Change in real yields Change in breakevens
Change in nominal yields
1. Year-to-Date Change in Yields
(Percent)
2. Inflation Breakeven
(Percent)
3. Decomposing Changes in Inflation Breakeven
(Percent)
4. Market-Implied Probability of Inflation Outcomes
(Percent, over five years)
… driven by higher expected inflation in the euro area, and with
somewhat higher inflation risk premia playing a role in the United
States.
Nominal yields have increased significantly, reflecting rising inflation
breakevens and real rates.
Five-year inflation breakevens have increased sharply since the
invasion …
The probability of high inflation outcomes has increased notably since
the previous GFSR.
Sources: Bloomberg Finance L.P.; Goel and Malik (2021); and IMF staff calculations.
Note: In panel 4, probabilities are derived from inflation caps and floors. EA = euro area; 5yr5yr = 5-year, 5-year forward; H1 = first half of the year; GFSR = Global
Financial Stability Report.
Figure 1.7. Drivers of Advanced Economy Bond Yields
–0.5
0.0
0.5
1.0
2.0
1.5

4.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
–1.0
2.0
0.0
1.0
–0.5
0.5
1.5
0
100
20
40
60
80
5 year 5yr5yr
United States (post-invasion)
5 year 5yr5yr
Euro area (post-invasion)
United States Euro area UK
Jan.
2021
Apr.
21
July
21
Oct.
21
Jan.
22
Apr.
22
Sharp rise in H1
Stabilization
Fed’s hawkish turn + virus concerns
Post-invasion
O
ct
. 2
02
1
G
FS
R
20
21
e
nd
Ap
ril
2
02
2
G
FS
R
US Euro area AustraliaUK Japan Canada
10
y
ea
r
5
ye
ar
5y
r5
yr
10
y
ea
r
5
ye
ar
5y
r5
yr
10
y
ea
r
5
ye
ar
5y
r5
yr
10
y
ea
r
5
ye
ar
5y
r5
yr
10
y
ea
r
5
ye
ar
5y
r5
yr
10
y
ea
r
5
ye
ar
5y
r5
yr
O
ct
. 2
02
1
G
FS
R
20
21
e
nd
Ap
ril
2
02
2
G
FS
R
O
ct
. 2
02
1
G
FS
R
20
21
e
nd
Ap
ril
2
02
2
G
FS
R
21 18
5
53
30
9
53
33
16
46
31
30
20
6
44
31
0
29
47
65
20
59
38
64
82

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9International Monetary Fund | April 2022
Inflation breakevens (a market-implied proxy for
future inflation) have risen significantly since the
beginning of the year on the back of sharply higher
commodity prices (Figure 1.7, panel 2). Real rates have
also increased in a number of advanced economies, on
expectations of tighter monetary policy.
The increase in inflation breakevens across countries
has been very pronounced at the five-year horizon. In the
euro area, such an increase appears to reflect significantly
higher expected inflation, while in the United States
higher inflation risk premia—an estimated proxy for
inflation uncertainty—seem to have also played a role
(Figure 1.7, panel 3). Meanwhile, the rise in inflation
breakevens at the five-year, five-year forward horizon
has been more contained so far, driven primarily by
higher inflation risk premia, suggesting that longer-term
inflation expectations continued to be largely anchored
despite the jump in commodity prices. However, pricing
in inflation options markets points to a notable increase
in the probability of high inflation—specifically, inflation
outcomes greater than 3 percent—since the time of the
previous GFSR (Figure 1.7, panel 4).
The market-implied expected path of policy has
risen significantly in advanced economies since the
beginning of the year and moved further upward since
the Russian invasion of Ukraine, as central banks
have taken steps to normalize monetary policy amid
record-high headline inflation (Figure 1.8, panel 1). In
the euro area, the European Central Bank (ECB) has
accelerated the pace of tapering its asset purchase pro-
gram, noting that interest rate increases could follow
some time after the end of asset purchases. The Bank
of Japan, by contrast, has maintained its ultra-loose
policy as inflation has remained subdued.
The Federal Reserve delivered its first policy rate
hike at its March FOMC meeting. In addition, the
median FOMC participant now anticipates the federal
funds rate to approach 2 percent by the end of the
year (Figure 1.8, panel 2). In real terms, however, the
FOMC-implied stance of policy is expected to remain
accommodative at least through 2023 (Figure 1.8,
panel 3). Even though the market-implied policy path
in 2022 is now above the FOMC participants’ assess-
ment of appropriate monetary policy, there is still a
risk of a possible repricing of the magnitude of the
policy cycle. Historically, once tightening is under way,
long-term interest rates eventually tend to move higher
(Figure 1.8, panel 4). Such an increase, especially if
driven by real rates, may lead to a sudden repricing of
risk that may weigh on economic prospects. Reportedly
reflecting concerns about the economic outlook, the
US Treasury yield curve has flattened significantly since
the beginning of the year, and certain segments of the
curve have inverted (Figure 1.8, panel 5).
The normalization of balance sheet policies may
present additional challenges to central banks. While
policy rates remain the main monetary policy tool, clear
communication on plans to unwind the unprecedented
expansion of central bank balance sheets—in terms of
timing, speed of reduction, and composition of both the
asset and liability sides—will be crucial to avoid unnec-
essary market volatility. To gauge the impact of balance
sheet normalization on long-term interest rates, investors
have focused on the 2017–19 quantitative tightening
(QT) experience, highlighting the risk of a sudden
increase in term premia given the larger size of the
Federal Reserve’s balance sheet and its footprint in some
market segments (Figure 1.9, panel 1). The unwinding
of the Federal Reserve’s balance sheet is expected to be
fast, with more than $1 trillion of assets (approximately
20 percent of the Treasury securities held in the Federal
Reserve System Open Market Account portfolio) matur-
ing in 2022 (Figure 1.9, panel 2).
While still low by historical standards, southern Euro-
pean countries’ spreads have widened since the ECB’s
announcements of its intention to scale back asset pur-
chases, underscoring the risk of market fragmentation in
the euro area. Between 2020 and 2021, accommodative
and supportive market conditions brought about by the
ECB’s asset purchase programs have helped push spreads
lower (Figure 1.9, panel 3). With fiscal deficits and
debt levels remaining relatively high in some countries,
additional fiscal stimulus in Europe is being considered
to cushion the impact of the war in Ukraine (including
future defense and climate spending) (Figure 1.9, panel
4). The wind-down of asset purchases may contribute to
a tightening of financial conditions.
Emerging Market Central Banks Face Further
Inflation Pressure
Even before the Russian invasion of Ukraine and the
associated surge in commodity prices, emerging market
central banks in Latin America and Europe were facing
rising inflation pressure. Inflation prints came in well
above central bank targets last year, outpacing inflation
forecasts (Figure 1.10, panel 1). To maintain market
confidence in their ability to meet their mandates,

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
10 International Monetary Fund | April 2022
Federal funds target rate
3-month rate 2 years forward (Eurodollar contract)
5-year rate 5 years forward
10y–2y spread Recessionary episodes Federal funds rate
Latest 2021 end Pre-invasion
FOMC projections: median dots (December 2021 meeting)
FOMC projections: median dots (latest)
Market expectations of policy rates
Neutral [nominal] rate estimate
Real projections: FOMC projections adjusted for expected inflation
Neutral [real] rate estimate
Figure 1.8. Increase in Advanced Economy Policy Rates
Market-implied expectations of policy rates have risen across advanced economies.
1. Policy Rate Expectations: Advanced Economies
(Percent)
2. Shift in US Policy Rate Projections: Nominal Rates
(Percent)
The FOMC assessment of appropriate monetary policy has also moved
significantly higher.
3. US Policy Rate Projections: Real Rates
(Percent)
Accounting for expected inflation, however, policy appears to still be
relatively accommodative for the current and following year.
Longer-term interest rates tend to move higher once policy tightening
is under way.
4. Long-Term Interest Rates and Policy Tightening
(Percent)
5. US Yield Curve Slope and the Federal Funds Rate
(Percent; percentage points)
The yield curve has flattened significantly since the beginning of the
year, reflecting concerns about the economic outlook.
–5
0
5
10
15
20
25
Sources: Bloomberg Finance L.P.; national authorities; US Federal Reserve; and IMF staff calculations.
Note: BOE = Bank of England; ECB = European Central Bank; FED = US Federal Reserve; FOMC = Federal Open Market Committee.
FED ECB BOE
97 07 12 17 221992 2002
La
te
st
20
22
e
nd
20
23
e
nd
20
24
e
nd
20
25
e
nd
20
26
e
nd
La
te
st
20
22
e
nd
20
23
e
nd
20
24
e
nd
20
25
e
nd
20
26
e
nd
La
te
st
20
22
e
nd
20
23
e
nd
20
24
e
nd
20
25
e
nd
20
26
e
nd
0.00
2.00
4.00
–1.00
0.00
2.00
1.00
0.00
2.00
1.00
3.00
0.50
1.00
1.50
2.00
2.50
3.50
3.00
–3.00
–2.00
–1.00
0.00
1.00
2023 2024 Longer term2022 2023 2024 Longer term2022
0
1
2
3
4
5
6
7
8
9
67 72 77 82 87 92 97 07 12 17 221962 2002

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11International Monetary Fund | April 2022
many central banks responded decisively and front-
loaded policy tightening—a crucial step, as evidenced
by the relative stability of longer-term inflation expec-
tations.6 Market participants were already pricing that
central banks in Latin America and eastern Europe
6Two notable exceptions are Argentina and Turkey, where inflation
expectations remain well above the inflation targets in the relevant
policy horizon.
would be able to halt or even reverse earlier hikes
within a one-year horizon on the back of an improve-
ment in the inflation outlook (Figure 1.10, panel 2).
Meanwhile, investor flows in local currency markets
were experiencing a nascent recovery.
However, the Russian invasion of Ukraine has
adversely affected the outlook for many emerging mar-
kets. As indicated in the April 2022 WEO, relative to
the January 2022 WEO Update, the inflation forecast
10-year real yield (TIPS-implied)
Scenario: no quantitative easing
(targeting TIPS market)
Greece Italy Portugal Spain
<1 year 1–5 years 5–10 years 10+ years France Germany Italy Spain Sum of other countries ECB government bond purchases 1. Impact of Quantitative Tightening on Real Rates: Decompression of Liquidity Premia (Percent) 2. Distribution of Residual Maturities of the Treasury Securities Held by the Federal Reserve (Percent; billions of US dollars) 3. Euro Area 10-Year Peripheral Spreads (Basis points, against German bunds) 4. European Central Bank Net Sovereign Purchases and Deficits (Percent of GDP) Southern European sovereign yields have exceeded pre-pandemic levels and spreads have widened. A repricing of risk is possible, as the effects of quantitative tightening on the path of interest rates remain uncertain. The Federal Reserve’s run-off potential in 2022 is approximately 20 percent of the Treasury securities held in the System Open Market Account (SOMA) holdings. Borrowing needs remain larger compared to pre-pandemic levels and vary across countries. Sources: Bloomberg Finance L.P.; Federal Reserve; national authorities; and IMF staff calculations. Note: ECB = European Central Bank; QE = quantitative easing; TIPS = Treasury Inflation Indexed Securities; T-sec = Treasury securities. Figure 1.9. A Challenging Normalization Process –1.5 –0.5 0.5 1.5 0 100 20 40 60 80 0 450 150 300 75 225 375 –1 9 1 3 5 7 Ja n. 2 01 4 Au g. 1 4 M ar . 1 5 50–60 basis point compression due to QE O ct . 1 5 M ay 1 6 D ec . 1 6 Ju ly 1 7 Fe b. 1 8 Se p. 1 8 Ap r. 1 9 N ov . 1 9 Ju ne 2 0 Ja n. 2 1 Au g. 2 1 Ja n. 2 01 9 Ap r. 1 9 Ju ly 1 9 O ct . 1 9 Ap r. 2 0 Ju ly 2 0 O ct . 2 0 Ja n. 2 0 Ap r. 2 1 Ju ly 2 1 O ct . 2 1 Ja n. 2 2 Ja n. 2 1 Asset-purchase announcements Scaling back asset purchases announcements T-sec under SOMA 2019 20 21 22f 23f Forecast 1,134 2,155 1,019 1,353 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E 12 International Monetary Fund | April 2022 for emerging market and developing economies for 2022 has been revised up 2.7 percentage points to 8.6 percent, while the GDP forecast for 2022 has been revised down 0.9 percentage point to 3.9 percent. The war in Ukraine has had a larger impact on economies in central and eastern Europe, where a notable tight- ening of financial conditions has been accompanied by currency interventions (and restrictions such as by Rus- sia and Ukraine), and a shift to an even more hawkish monetary policy stance in some cases. The rise in com- modity prices has been swiftly felt in most countries with direct trade links to Russia and Ukraine, creating further upside risks to inflation. In addition to a shift to a more hawkish stance of monetary policy, some countries (such as Egypt) have also taken the oppor- tunity to use the exchange rate as a shock absorber.7 By contrast, commodity exporters across emerging markets, such as Brazil, Chile, and South Africa have 7Other countries also had to resort to measures to stem outflows of foreign exchange given the spike in demand for foreign exchange and logistical difficulties in sourcing foreign exchange. For example, Kazakhstan banned people leaving the country with more than $10,000 and imposed restrictions on gold and silver departures. seen an improvement in their terms of trade and a relatively milder impact on financial conditions. This has provided central banks with more space to calibrate monetary policy to domestic developments. Emerging market economies in Asia that have limited direct links to Russia and Ukraine and a more benign inflation outlook have continued with their more delayed and gradual policy normalization. Transmission Channels of the War through Financial Intermediaries and Markets The Russian invasion of Ukraine and ensuing sanctions have already had an impact on financial intermediaries, firms, and markets directly or indirectly exposed to the war. Europe bears a higher risk than other regions due to its proximity, reliance on Russia for energy needs, and non-negligible exposure of some banks and other financial institutions to Russian finan- cial assets and markets. But the war is also generating broader concerns well beyond Europe. Rising risk aversion has led to flight-to-quality flows and signs of strains in dollar-funding markets. Extreme volatility in commodity markets has resulted in ripple effects LatAm CEE EM Asia Figure 1.10. Inflation and Interest Rates in Emerging Markets Consensus expects that the inflection point for inflation prints is near. Policy-implied paths differ substantially among regions. 1. Deviation from Target for Inflation (Percent, average) 1 3 5 7 11 9 Dec. 2018 June 19 June 20 June 21 June 22 Dec. 19 Dec. 20 Dec. 21 Dec. 22 1m 3m 1 y 3y6m 2 y 5y 1m 3m 1 y 3y6m 2 y 5y 1m 3m 1 y 3y6m 2 y 5y Forecasts LatAm Oct. 2021 GFSR Latest Pre-invasion CEE EM Asia 2. Market-Implied Policy Rates (Percent) Sources: Bloomberg Finance L.P.; Consensus Forecasts; and IMF staff calculations. Note: Both charts are based on a sample of countries from CEE = Central and Eastern Europe; EM Asia = emerging market Asia; LatAm = Latin America. Panel 1 uses the upper limit of the inflation targeting framework where available. GFSR = Global Financial Stability Report. –4 –2 0 2 4 6 8 C H A P T E R 1 T h E F I N A N C I A L S T A B I L I T Y I M P L I C A T I O N S O F T h E w A R I N U k R A I N E 13International Monetary Fund | April 2022 across global markets and financial intermediaries, often magnified by poor liquidity, leading to lower risk appetite, rising counterparty risk concerns (for example, in relation to commodity financing and derivatives), and supply chain disruptions. The prospect of a Russian default on government debt and the removal of Russian assets from global indices would have implications for emerging market capital flows. Cyberattacks have become a first-order concern for financial institutions and policymakers alike. These factors can operate as shock amplifiers and, in some cases, lead to severe market disruptions. Foreign Banks’ Direct Exposures to Russia and Ukraine: Relatively Modest, in Aggregate Direct exposures of foreign banks to Russia and Ukraine appear to be relatively modest, in aggregate (Figure 1.11, panel 1).8 As of the third quarter of 2021, claims of foreign banks on Russian residents totaled about $120 billion, with 60 percent in foreign currencies. For Ukraine, exposures were relatively small at $11 billion. The vast majority of these exposures were held by euro area banks. For some countries, these exposures were economically significant, as individual banks play an active role in the Russian banking system (Figure 1.11, panel 2). Because they operate as subsidiaries, however, they typically fund themselves locally; as a result, intra-group loans are generally small. The market capitalization of European banks declined sharply after the Russian invasion (Figure 1.11, panel 3). While banks with large exposures to Russia and Ukraine experienced the largest declines, an index of European bank equity prices fell over 20 percent after February 24, reflecting in part concerns about a deterioration of the economic and profitability prospects.9 By contrast, equity prices of US banks dropped only about 8 percent at the worst point. 8The actual exposures are likely higher, as some countries are not included in the aggregate data. However, according to bank dis- closures or statements in 2022:Q1, exposures have likely decreased since 2021:Q3. 9The cost of equity (CoE) for European banks increased from 11 percent to 16.5 percent after the invasion, before recovering to modestly above the pre-invasion level. A capital asset pricing model shows that the increase in CoE has been driven by a rise in the European equity risk premium and amplified by higher sensitivity (beta). This is consistent with higher expected losses associated with Russian exposures, alongside a more challenging macroeco- nomic outlook. Meanwhile, the increase in European bank credit default swap (CDS) spreads has been more modest, suggesting that investors expect the impact of the war and sanctions on banks’ balance sheet and capital to be manageable. Banks with Russian subsidiaries can choose to either exit the market entirely or maintain their presence but prepare for a sharply worsening revenue and asset quality outlook. The exit strategy is estimated to reduce the common equity Tier 1 (CET1) ratio at the group level by an average of 20 basis points, with an impact about four times larger for the most exposed bank (Figure 1.11, panel 4).10 However, cross-border exposures are likely to be either pulled back or experience some losses, in which case the total impact could reach an average of 80 basis points (about 2½ times the impact for the most exposed bank). Indirect Exposures: More Difficult to Assess Banks’ indirect exposures are more difficult to identify and assess because they are less well known (especially the extent of interconnectedness) and hard to quantify in the absence of detailed and consistent disclosures by country or specific activity types. The risk is that indirect exposures could be meaningful and surprise investors once revealed, leading to a sharp rise in counterparty risk and risk premia. These exposures could result from activities such as investment banking and wealth management, derivatives (including com- modity derivatives),11 and off-balance-sheet exposures related to supply chain or commodity financing, as well as contingent liabilities and guarantees.12 In some cases, these exposures to Russian counterparties could be large. For example, foreign exchange swap and forward contracts, unlike other derivative instruments, involve the exchange of notional amounts and are akin to collateralized lending. As such, gross positions mat- ter, as they expose institutions to significant counter- party and settlement risks, notably in situations where foreign currency settlement is restricted. 10The exercise assumes loss of equity, intra-group funding, and subordinated debt at the Russian subsidiary level, and de-consolidates the associated risk-weighted assets. Loss from cross-border exposures was considered as an additional shock, assum- ing a 100 percent haircut in the worst scenario. 11Commodity derivative exposure from euro area banks that are designated as significant institutions stood at 52 million euros, according to an ECB assessment as of March 15, 2022. 12Typically, trade finance has public or private insurance as risk mitigation. G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E 14 International Monetary Fund | April 2022 Before the war, Russian banks had entered into foreign exchange swaps and forwards contracts with foreign dealer banks. Typically, Russian banks would lend US dollars against a pre-agreed amount of Russian rubles (the gross notional amount), as they received large amounts of dollar deposits (and, to a lesser extent, euros) from their clients.13 The total gross notional amount of over-the-counter foreign exchange swaps 13Banks in Russia had around $220 billion US dollar deposits as of the end of September 2021, according to Bank for International Settlements locational banking statistics. and forwards between Russian banks and foreign dealer banks amounted to about $69 billion at the end of 2021 (Figure 1.12, panel 1, first bar, black diamond). To the extent that foreign dealer banks have received dollars, a default by Russian banks would have limited spillovers in the foreign exchange derivatives market, as foreign banks would be left holding US dollars. Even if that is the case, however, the termination of the foreign exchange deriva- tives exposures may leave both foreign and Russian banks with unhedged exposures. The Russian banks would be left with a currency mismatch against their domestic depositors, while foreign banks would have to find new 1 2 3 4 5 6 7 8 9 10 11 Foreign banks with largest Russian subsidiaries International claims: Russia Local claims: Russia Total: Russia International claims: Ukraine Local claims: Ukraine Total: Ukraine Exposure to Russia Exposure to Ukraine Exposure to other CEE countries Exposed to other CEE countries Exposed to Russia and Ukraine All European banks Loss of equity and intragroup funding Loss of equity and intragroup funding, with 100% haircut on cross-border exposures Figure 1.11. Foreign Bank Exposures to Russia and Ukraine Direct exposures to Russia and Ukraine are modest in aggregate ... 1. Foreign Banks’ Gross Claims on Russia and Ukraine (Billions of US dollars) 0 140 20 40 60 80 100 120 0 35 5 10 15 20 25 30 ... but are sizable at some banks. 2. Banks with Largest Exposures to Russia, Ukraine, and Central and Eastern Europe Countries (Percent of total assets) 0 60 10 20 30 40 50 There have been sharp declines in bank stocks and a relatively modest increase in credit default swaps ... 3. Stock Price and Credit Default Swap Spreads (Index, February 23, 2022 = 100; basis points) ... as the capital impact appears manageable for most. 60 120 70 80 90 100 110 4. Foreign Bank Exit: Potential Impact on Group CET1 Ratios (Basis points) –250 0 –200 –150 –100 –50 30 230 80 130 180 Sources: Bank for International Settlements, Consolidated Banking Statistics; European Banking Authority; Bloomberg L.P.; individual bank disclosures; and IMF staff calculations. Note: In panel 3, the blue-colored lines on the right chart refer to three different banks with material exposures to Russia and Ukraine. In panel 4, see footnote 15 for details. Data labels use International Organization for Standardization (ISO) country codes. CDS = credit default swaps; CEE = central and eastern Europe; CET1 = Common Equity Tier 1. Total FRA ITA AUT USA JPN DEU NLD CHE GBR KOR Right scale Left scale Ba nk 1 * Ba nk 2 Ba nk 3 Ba nk 4 Ba nk 5 Ba nk 6 Ba nk 7 Ba nk 8 Ba nk 9 Ba nk 1 0 Ba nk 1 1 Ba nk 1 2 Ba nk 1 3 Ba nk 1 4 Ba nk 1 5 Ba nk 1 6 Ba nk 1 7 Ba nk 1 8 Ba nk 1 9 *Total = 89% 03 J an . 17 J an . 31 J an . 14 F eb . 28 F eb . 28 M ar . 14 M ar . Equities 03 J an . 17 J an . 31 J an . 14 F eb . 28 F eb . 14 M ar . 28 M ar . CDS C H A P T E R 1 T h E F I N A N C I A L S T A B I L I T Y I M P L I C A T I O N S O F T h E w A R I N U k R A I N E 15International Monetary Fund | April 2022 instruments to hedge any outstanding ruble exposures. Outstanding amounts of over-the-counter interest rate derivatives, which require only an exchange of interest payments, are generally lower than foreign exchange gross notional amounts, and clearing requirements help to con- tain counterparty risk exposures (Figure 1.12, panel 2). Nonbank Financial Intermediaries: Coping with a Potential Russian Default Foreign nonbank financial intermediaries (NBFIs) had sizable investments in Russian assets, holding about one-fifth of its total sovereign debt, half of its corporate debt, and more than 40 percent of Russian equities as of the fourth quarter of 2021 (Figure 1.13, panel.1).14 Within the NBFI sector, open-end investment funds (OEFs), which offer mostly daily liquidity and are 14The estimate for equities is likely to be higher, as there is only data available for the holdings of foreign open-end funds, with the latter holding an estimated 40 percent of the market cap of Russian equities. therefore at greater risk of redemption pressures, have exposures to Russian equities of about $100 bil- lion, the vast majority of which is held by US funds (Figure 1.13, panel 2). OEFs also have a combined $34 billion in fixed-income assets, about two-thirds of which is held by European funds. As a share of total assets, however, their exposure to Russia is small. Even for European funds, which display the largest portfolio shares in Russian debt and equities, aggregate exposures are less than 2 percent of funds’ assets. Within the OEFs, emerging-market-dedicated funds hold the vast majority of Russian debt and equity. However, even these funds have maintained a cau- tious stance on their exposures to Russian debt since the Crimea occupation in 2014, particularly for the hard-currency bond funds subcategory (Figure 1.13, panel 3). Emerging market dedicated funds reduced their share of Russian debt from over 10 percent prior to 2014 to just over 4 percent in 2022. In fact, heading into the 2022 Russian invasion, these funds had (on average) an underweight position compared to their RUB USD EUR CNY Other Total RUB USD EUR Other Total Figure 1.12. Over-the-Counter Derivative Exposures of International and Domestic Banks in Russia, End-2021 Foreign exchange derivative exposures of foreign dealer banks to banks in Russia is significant ... 1. Foreign Exchange Swaps and Outright Forwards (Currency on Either Leg of the Contract) (Billions of US dollars; gross notional amounts) Vis-à-vis foreign dealer banks Vis-à-vis non- bank financial institutions Vis-à-vis non- financial institutions Vis-à-vis other banks in Russia Vis-à-vis foreign dealer banks Vis-à-vis non- bank financial institutions Vis-à-vis non- financial institutions Vis-à-vis other banks in Russia 2. Over-the-Counter Interest Rate Derivatives (Single Currency Forward Rate Agreements, Swaps, and Options) (Billions of US dollars; gross notional amounts) ... while over-the-counter interest rate derivative exposures are smaller (and less risky). 0 180 20 40 60 80 100 120 140 160 0 180 20 40 60 80 100 120 140 160 Sources: Central Bank of Russia; and IMF staff calculations.  Note: As foreign exchange swap and forward contracts involve the exchange of two currencies, the sum of outstanding notional amounts across individual currencies (for either leg of the contract) in panel 1 is exactly double the total outstanding amount. Foreign dealer banks include the subsidiaries and branches of these banks located in Russia. Over-the-counter interest rate derivatives are generally subject to clearing requirements, although for contracts in Russian rubles clearing has only been mandatory for interest rate swaps since the last quarter of 2021. Such swaps typically constitute the bulk of outstanding amounts (>75% of the global total).
CNY = Chinese yuan; EUR = euro; RUB = Russian ruble; USD = United States dollar.

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
16 International Monetary Fund | April 2022
emerging market benchmark (more on this follows).
In contrast to emerging-market-dedicated funds, funds
benchmarked to global indices had a much smaller
exposure to Russia (in both absolute and relative terms),
with an average 0.2 percent of their assets invested
in Russian debt in 2022.15 On equities, the share of
15Separately, unconstrained global multi-sector bond funds
(MSBFs) hold over 1 percent of Russia’s total sovereign debt stock,
but this exposure is also small when measured as a percentage
of assets. However, these funds may have exposure to derivative
contracts, which could be subject to greater losses.
Russian exposure in emerging-market-dedicated funds
stood at 4 percent of total assets before the invasion,
while for global equity funds it was less than 0.2 per-
cent. Since the Russian invasion of Ukraine, the very
sharp drop in valuations of Russian assets has dramat-
ically reduced the market value of investment funds’
exposures to Russia. Some regulators have started to
consider options to isolate Russian assets from broader
portfolios by, for example, allowing the separation
of the Russian exposures into so-called side pockets,
which are portfolio tranches exclusively owned by
Rest (primarily domestic)
Foreign open-end funds Other foreign NBFIs
Global bond funds EM bond funds Global equity funds EM equity funds
Mixed funds Equity funds Fixed income funds
Other funds Share (right scales)
1. Russian Sovereign Debt, Corporate Debt, and Equities
(Billions of US dollars; percent)
2. Open-End Investment Fund Exposure to Russian Sovereign Debt,
Corporate Debt, and Equities
(Billions of US dollars, left scales; average portfolio share, percent,
right scales)
3. Open-End Bond Fund Portfolio Allocation to Russia
(Percent of assets)
4. Open-End Equity Fund Portfolio Allocation to Russia
(Percent of assets)
The share of Russian bonds in the portfolios of emerging-market-
dedicated bond funds has declined since 2015 and is negligible for
global funds …
Foreign nonbank financial intermediaries hold a sizable amount of
Russian securities …
… with US and European investment funds accounting for most of the
exposures.
… and a similar pattern prevails for equity funds.
Sources: Arslanalp and Tsuda (2014, updated); Bloomberg Finance LP; Haver Analytics; JPMorgan Chase & Co.; Morningstar; and IMF staff calculations.
Note: In panel 1, the “other foreign NBFIs” category for corporate bonds includes all intermediaries that are not open-end funds, including sovereign wealth funds,
close-end funds, pension funds, hedge funds, and others. The “rest” category in panel 1 for equities also includes foreign NBFIs outside of open-end funds due to the
lack of available data. The market cap of the MOEX index is used as a proxy for the total value of Russian equities. The total value of both Russian sovereign and
corporate bonds outstanding includes both foreign and domestic currency bonds. EM = emerging markets; EU = European Union; NBFIs = nonbank financial
intermediaries; OAE = other advanced economies; US = United States.
Figure 1.13. Exposure to Russian Assets by Foreign Nonbank Financial Intermediaries
0
350
50
100
150
200
250
300
0
80
20
40
60
0
25
5
10
15
20
0.0
1.0
0.2
0.4
0.6
0.8
–0.3
1.2
0.2
0.7
0
12
2
4
6
8
10
0
8
2
4
6
2013 22
Sovereign bonds Corporate bonds Equities EU OAE US EM EU OAE US EM
Fixed income
exposures
Equity exposures
14 15 16 17 18 19 20 21 2013 2214 15 16 17 18 19 20 21
9
10
81
4
50
46
40
60

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17International Monetary Fund | April 2022
existing investors and are temporarily not available for
redemption.16
Some NBFIs, such as specialized insurers and leasing
companies, may also be facing greater risks in the areas
of cyber underwriting, trade credit, and aircraft leasing.
The war in Ukraine has intensified the risk of offensive
cyber operations, with a potentially adverse impact on
financial stability in the region and beyond. Despite
the relatively small size of cyber insurance (estimated
at $8 billion globally), it has experienced rapid growth
amid concerns about the uncertainty of expected losses
against which insurers have to reserve and hold cap-
ital.17 Aircraft leasing companies, many of which are
domiciled in Ireland, are also exposed to potential large
losses if Russia refuses to return leased aircraft. Finally,
foreign providers of trade credit are also exposed to
Russia, with an estimated $16 billion of trade credit as
of the last quarter of 2021.
Foreign sanctions as well as capital controls and
other retaliatory measures imposed by Russia have
increased risks for foreign investors in Russian securi-
ties. Payments to foreigners are not explicitly forbidden
by the current set of sanctions, but actions taken by
Russian and other international securities depositories
(ICSDs), along with the freezing of some of Russia’s
international reserves, have made payments more
difficult.18 At the time of writing, Russian authorities
have continued servicing Russia’s foreign law debt
in hard currency but have suspended the transfer of
payments to foreigners on local law ruble-denominated
bonds. The latter action has not created major com-
plications to foreign law debt given that foreign law
bonds and CDS do not contain cross-default terms
with local law bonds (Figure 1.14, panel 1). However,
further sanctions could prevent bonds from trading in
16The United Kingdom’s Financial Conduct Authority is
currently discussing the option of side pockets with asset manag-
ers (FCA 2022). In general, side pockets and gates—temporary
redemption stops—are permitted in several European jurisdictions
as liquidity management tools used by open-end investment funds
(ESMA 2020).
17The limited loss history of cyber events, the unreliability of
past data when predicting future events, and the possibility of a
large-scale attack where losses are highly correlated across firms and
sectors make it difficult to write comprehensive policies (Granato
and Polacek 2019).
18The US Treasury has stated that US persons are authorized to
receive interest, dividend, or maturity payments on debt or equity
of the Central Bank of the Russian Federation, the National Wealth
Fund of the Russian Federation, and the Ministry of Finance of the
Russian Federation through May 25, 2022.
the secondary market, which would hamper the CDS
settlement process.
In addition to disappearing liquidity and rising
credit risk, investors face significant challenges in
terms of the valuation of their financial instruments.
For example, some foreign investors have positions in
non-deliverable forwards (NDFs) that settle in dollars
but use the onshore foreign exchange rate as the ref-
erence rate. The NDF positions can help them hedge
their currency exposures without having to sell their
highly illiquid positions in local-currency-denominated
assets. Since the start of the war, the Russian central
bank has kept tight control on the onshore foreign
exchange market,19 and the Ukrainian central bank
has not updated the daily foreign exchange rates. The
Russian and Ukrainian exchange rates in offshore mar-
kets have diverged from the onshore rates, rendering
the NDFs as ineffective hedges (Figure 1.14, panel 2).
The sanctions and valuation differences between
onshore and offshore markets can also be a problem
for foreign banks that have foreign exchange derivatives
exposures vis-à-vis Russian banks.
The reduced investability of Russian assets has led
to their exclusion from multiple benchmark indices
largely used by emerging-market-dedicated funds.20
The sharp drop in the liquidity of Russian securities
and the reduced convertibility of the ruble were some
of the key reasons behind the decisions of benchmark
providers. Global bond benchmarks (as opposed to
emerging-market-specific benchmarks) are reliant on
Russia maintaining an investment-grade rating, which
is no longer the case. Environmental, social, and gover-
nance (ESG) related indices have also excluded Russian
assets. While these ESG indices are relatively smaller in
size, they are growing fast and reflect investors’ increas-
ing focus on the ESG dynamics for emerging markets.
Finally, Ukraine’s inclusion in the JPMorgan Govern-
ment Bond Index-Emerging Markets (GBI-EM) index
family, which was scheduled for March 31, 2022,
is now subject to a further review given the current
circumstances. This inclusion was expected to bring
additional flows to the local market and help with
market deepening.
19Normally, the Russian central bank provides daily fixings
(official rate) of the exchange using transactions in the local market.
However, trading in local markets has been severely impaired by
various restrictions such as the shutdown of the stock exchange for
several weeks.
20Similar issues apply to Belarusian assets.

https://home.treasury.gov/policy-issues/financial-sanctions/faq/updated/2022-03-02

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
18 International Monetary Fund | April 2022
The index exclusion of Russia is a notable event
because benchmark-driven investors have become a key
source of intermediating cross-border flows to emerg-
ing markets.21 While the index exclusion adds to price
pressures and illiquidity, Russia’s weight in the indices
has declined sharply in the past few years. Its median
weight across major indices dropped from 10 percent
during the global financial crisis to just 3 percent before
the Russian invasion of Ukraine, and less than 1 percent
immediately thereafter, largely due to valuation declines
(Figure 1.15, panel 1).
Russia’s exclusion from benchmarks could lead to
some positive portfolio reallocation flows to other
emerging markets, as their benchmark weight will
mechanically increase. Investors could also choose to
reallocate funds to other emerging markets that shared
similarities with Russia before the war. For instance, the
2014–15 Russian annexation of Crimea led to a foreign
investor exit from Russian local assets, while foreign
ownership in other high-yielding emerging markets rose
21JP Morgan’s March 2022 client survey showed that nearly half
of participants plan to divest as much of their Russian debt holdings
as possible and hold the rest off-index, while nearly a quarter plan to
continue investing.
at the same time (Figure 1.15, panel 2). Investors could
also gain exposure to countries that benefit from the
current macro backdrop, such as commodity exporters.
Commodity Price Volatility Amplified by Commodity
Trade Finance and Derivatives Exposures
The ongoing war in Ukraine, associated sanctions,
market participants’ actions in response to the global
outcry, and rising counterparty risk have caused severe
disruptions in commodity markets and supply chains
across the globe (Blas 2022).22 Amid sharply rising
volatility, prices have skyrocketed across the commodity
complex, causing severe pressures in commodity financ-
ing and derivatives markets. Shipping costs of com-
modities have increased, and higher commodity prices
have raised the financing needs of commodity traders
and those involved along the supply chain. In addition,
users of commodity derivatives (including commodity
producers using futures or options for hedging purposes,
commodity trading firms, dealer banks, levered investors
22The European Union banned imports of certain metals from
Russia and the United States banned oil, gas, and coal imports.
15 16 17 18 19 20 212014
Non-RUB fallback, not settled on NSD
Non-RUB fallback, NSD settled
RUB fallback, NSD settled
Ruble (Bloomberg London Composite)
Ruble onshore (Moscow Exchange)
Difference (right scale)
Figure 1.14. Investor Challenges in Russian Security Markets
Russian hard currency bonds trade in three tiers depending on
recovery assumptions.
1. Russian Hard Currency Bonds
(Average price of bonds in tier)
0
140
20
40
60
80
100
120
Sanctions and other restrictions have created a notable disconnect in
the ruble market.
2. Dollar/Ruble Exchange Rates
(Rubles per dollar; percent difference)
–0.2
0.8
0
0.2
0.4
0.6
0
160
20
40
60
80
100
120
140
Sources: Bloomberg Finance L.P.; and IMF staff calculations.
Note: In panel 1 “RUB fallback” refers to a provision that allows these bonds to be repaid in rubles under certain conditions. NSD = Russian National Settlement
Depository; RUB = rubles.
Jan. 2022 Feb. 22 Feb. 22 Mar. 22 Apr. 22Mar. 22

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19International Monetary Fund | April 2022
like hedge funds, and investment funds) have faced
massive margin calls on short positions in response to
huge swings in commodity prices, testing the resilience
of corners of global financial markets that were little
known by the broader public only a few weeks ago (see
Box 1.1 on the nickel market disruption).23
Dealer banks play a crucial role and have significant
exposures in commodity markets, so there is a risk they
may become a propagation channel of commodity mar-
ket disruptions. They provide collateralized funding to
finance the shipment of commodities. In addition, they
provide leverage to some investors and act as interme-
diaries in commodity derivatives markets. For example,
when commodity producers enter into a (short) future
position to hedge against a drop in (future) commodity
prices, dealer banks take the opposite side (long) of this
trade. In turn, they then hedge their book by entering
23Commodity producers are important users of commodity
derivatives, often hedging against a drop in future commodity prices.
Other participants in the commodity derivatives market include
large commodity trading houses (see ECB 2017) and leveraged
investors. Large investment banks operate as intermediaries in com-
modity financing and commodity derivatives, as well as providers of
leverage to some of these investors.
into an opposite trade (for example, on an exchange).24
Furthermore, they often offer lines of credit to their cli-
ents, which can be used at times of acute liquidity needs.
A concern raised by some market participants is
that, in response to large swings in commodity prices,
differences in initial margin modeling and the prevalence
and frequency of posting variation margins appear to be
incentivizing some derivative users to trade bilaterally
with broker dealers instead of centrally cleared trades,
because doing so may offer lower likelihood of large
increases in initial margins and of demand for posting
more variation margins in times of stress.25 As a result,
dealer banks may be exposed to higher margin calls by
24In the event of a sharp increase in prices, banks are owed money
from commodity producers that face margin calls on short futures
positions, but also owe money to the exchange on their own short
positions used as a hedge—so they themselves face margin calls.
If the producers are unable to meet margin calls, the dealers are
caught with unhedged exposures.
25Initial margins are collateral required to protect a transacting
party in the event of default by the other counterparty that could
result from a future change in the mark-to-market value. Variation
margins are collateral required to protect the party for the current
exposure and depend on the mark-to-market value of the derivatives,
which can change over time.
EM local currency bonds
EM hard currency bonds
Equities
Global bonds
Corporate
Average (BRA, COL, MEX, IDN, ZAF)
RUS
Figure 1.15. Impact from Russia’s Exclusion from Global Benchmark Indices
Russia’s weight in global benchmark indices has declined sharply over
the years.
1. Weight of Russia in Different Global Benchmark Indices
(Percent)
0
25
5
10
15
20
Foreign ownership trends can diverge meaningfully between Russia
and other emerging markets.
2. Foreign Ownership of Local Currency Bonds
(Percent)
15
35
17
19
21
23
25
27
29
31
33
Sources: Bloomberg Finance L.P.; JPMorgan Chase & Co.; and IMF staff calculations.
Note: In panel 1, EM local currency bonds refers to JPMorgan Government Bond Index, EM hard currency bonds refers to JPMorgan Emerging Market Bond Index,
global bonds refers to Bloomberg Barclays Global Aggregate Index, and corporate refers to JPMorgan Corporate Emerging Market Bond Index. Data labels use
International Organization for Standardization (ISO) country codes. EM = emerging markets.
1999 2001 03 05 07 09 11 13 15 17 19 21 14 15 16 17 18 19 20 212013

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20 International Monetary Fund | April 2022
the exchanges and central counterparty clearing houses
compared to what they collect from clients, adding to
banks’ liquidity needs.26 More broadly, the danger is that
liquidity risk may morph into counterparty credit risk,
thus lowering dealers’ balance sheet capacity and raising
the cost of intermediation across a number of markets.
Another possible pressure point is related to con-
centration and interconnectedness. The number of
dealer banks globally active in commodity markets has
declined in recent years. These banks provide credit
and liquidity to, among others, a small group of large
energy trading firms that operate globally across a
number of commodity markets. These firms are largely
unregulated, mostly privately owned, and highly reliant
on financing by dealer banks to operate. Market partic-
ipants have also expressed concerns about dealer banks’
concentrated positions with respect to assessment of
aggregate exposures and risk management practices.27
In addition, available data suggest that investors may be
26At this point, it remains unclear whether these trades are
executed over the counter but still centrally cleared, or both executed
and cleared over the counter.
27The Division of Trading and Markets of the US Securities and
Exchange Commission issued a statement on March 14, 2022, urg-
ing broker-dealers and other market participants to remain vigilant
regarding market and counterparty risks that may surface during
periods of heightened volatility and global uncertainties.
growing concerned about credit availability and liquid-
ity positions of commodity trading firms amid large
commodity price moves (Figure 1.16, panels 1 and 2).
Strains in commodity markets may also have adverse
effects for end users like commodity producers and
consumers, including manufacturers reliant on raw
material inputs as well as ultimate consumers. Amid
supply chain disruptions and large price swings, banks
may become less willing to finance commodity ship-
ments, and the cost of hedging through futures and
options may become prohibitively expensive for some
producers. In addition, in the event of default on a
derivatives contract by a counterparty, smaller clearing
members of exchanges may themselves face risk of
default, adding strains to the system.
Rising Liquidity and Funding Risks
There are some signs that the sharp rise in market vol-
atility, severe disruptions in commodity markets, and the
perception of rising counterparty risk may be starting to
weigh on dealer banks’ balance sheet capacity and appe-
tite for intermediation, with implications for liquidity and
funding conditions as well as broader market functioning.
Tensions in short-term dollar funding markets
have been limited so far, but strains are beginning
Weekly percent change
Gunvor 2026
Trafigura 2026
Glencore 2026
Cargill 2026
Louis Dreyfus 2028
1. Commodity Price Change
(Percent)
2. Bond Performance of Key Energy Commodities Trading Companies
(Price as percent of face value)
The spike in commodity price volatility … … causes stress for commodity trading companies
Sources: Bloomberg Finance L.P.; and IMF staff calculations.
Note: In panel 2, the bond prices of Gunvor, Glencore, and Cargill are quoted in US dollars; the bond prices of Trafigura and Louis Dreyfus are quoted in euros.
Figure 1.16. Commodity Trading Companies Have Been Exposed to a Spike in Volatility
–15
15
–10
–5
0
5
10
65
105
80
75
70
85
90
95
100
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21International Monetary Fund | April 2022
to emerge. Reportedly reflecting both precautionary
motives to bolster liquidity positions as well as growing
concerns about credit risk, spreads in short-term dollar
funding markets have widened. In US unsecured
money markets, LIBOR-OIS and FRA-OIS spreads
have widened since the announcement of sanctions,28
but they are still well below levels seen in early 2020.
Issuance of financial and nonfinancial commercial
paper has risen, leading to increased borrowing costs
(Figure 1.17, panel 1). By contrast, secured US
money markets (repo) have not displayed signs of
stress thus far.
28LIBOR is the London interbank offered rate, OIS stands for
overnight index swap, and FRA stands for forward rate agreement.
Similarly, international dollar funding conditions, as
measured by the cross-currency swap basis, have tight-
ened since late February, but spreads remain well below
pandemic levels (Figure 1.17, panel 2). The actions
taken to freeze the Central Bank of Russia’s reserves
and disconnect a number of Russian banks from
SWIFT have also been mentioned as factors contribut-
ing to spread widening.29 Amid rising risk aversion and
29Russian banks and the central bank have traditionally been
net suppliers to dollar funding markets. However, the impact of
the disconnection of Russian banks from SWIFT and freezing of
central bank assets on dollar funding markets has been relatively
modest thus far. This is mainly due to the large US dollar oversupply
in funding markets; other lenders have taken up the slack that the
departure of Russian funding created.
Bloomberg JPMorgan
EUR JPY GBP
US Treasury German Bunds
Bank CP to T-bill spread
Corporate CP to T-bill spread
FRA-OIS 3 month
Figure 1.17. Short-Term Dollar Funding Tensions and Market Liquidity
US money market conditions have tightened somewhat …
1. US Money Market Rate Spread
(Basis points)
0
200
400
… and international dollar funding conditions have also shown some
strains.
2. Cross-Currency Basis Spreads, Three-Month
(Basis points)
Bid-ask spreads of high-quality government bonds are the widest since
the peak of the COVID-19 crisis.
3. Bid-Ask Spreads
(Basis points)
0.0
1.0
0.5
Divergence from fair value models reflects traders’ unwillingness to
provide liquidity.
4. Liquidity Indices: Root Mean Square Error of the Fitted
US Treasury Yield Curve
(Basis points)
0
1
2
3
4
Sources: Bloomberg Finance L.P.; JPMorgan Chase & Co.; and IMF staff calculations.
Note: In panel 1, commercial papers (CPs) are AA bank 90-day CPs and A2P2 nonfinancial corporate 90-day CPs. The FRA-OIS spread measures the gap between the
US 3-month forward rate agreement and the overnight index swap rate. In panel 2, LIBOR-indexed cross-currency basis spreads are used for JPY and GBP prior to
February 2021. In panel 4, root mean square error is the measure between fair-value model yields and actual Treasury yields observed. GBP = British pound;
EUR = euros; JPY = Japanese yen; T-bill = US Treasury bill.
Jan. 2020 July 20 Jan. 21 July 21 Jan. 22
Jan. 2020 July 20 Jan. 21 July 21 Jan. 22 Jan. 2020 July 20 Jan. 21 July 21 Jan. 22
Jan. 2020 July 20 Jan. 21 July 21 Jan. 22
Feb. 23, 2022Feb. 23, 2022
Feb. 23, 2022
–150
–100
–50
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50
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22 International Monetary Fund | April 2022
strong precautionary demand for high-quality collat-
eral, 10-year euro area swap spreads have widened to
levels not seen since 2011.
Despite higher volatility and some strains in fund-
ing markets, there are no signs of the “dash-for-cash”
dynamics that emerged in March 2020, and the financial
system appears more resilient to withstand liquidity
and funding shocks. Global liquidity remains at record
high levels in advanced economies, and banks are better
capitalized and more liquid with a large surplus of
reserves. In addition, central banks have tools to alleviate
stresses in funding markets. Activation of standing swap
lines between central banks and government paper repo
lines—the US Federal Reserve’s standing repo facility
(SRP) and the Foreign and International Monetary
Authorities (FIMA) repo facility, as well as the ECB’s
Eurosystem repo facility for central banks—can act as a
backstop for dollar (and euro) funding pressures.30 How-
ever, the vulnerabilities identified during the COVID-19
pandemic remain largely unaddressed at this point.
Given higher uncertainty and faster Federal
Reserve policy tightening, market liquidity condi-
tions of high-quality government bond markets have
deteriorated based on multiple metrics. Price-based
liquidity metrics, such as bid-ask spreads and fitting
errors of yield curve models, have worsened, reflecting
market-makers’ unwillingness to hold inventories under
a higher volatility environment (Figure 1.17, panels 3
and 4). Further deterioration of market liquidity and
functioning could amplify a repricing of duration risk.
There also might be a risk of tighter funding conditions
due to a close link between market liquidity and fund-
ing liquidity (Brunnermeier and Pedersen 2009).31
Cyber Risks: A Critical Threat
The war in Ukraine has raised acute concerns about
cyber operations. Cyberattacks targeting Ukraine go
back several years. In 2017, the NotPetya malware attack
originally aimed at critical infrastructure in Ukraine
spilled over and caused supply chain disruptions and
worldwide losses estimated at about $10 billion.32
30The usage of the US Federal Reserve reverse repo facility as of
March 25 stood at a level similar to February 23 ($1.7 trillion).
31A decline in market liquidity leads to higher price impact and
higher volatility, and a volatility shock may lead to higher haircuts
and funding rates. As funding becomes scarce, market makers find
it difficult to obtain leverage to finance their inventories. There is a
feedback mechanism linking market liquidity and funding liquidity.
32According to multiple sources, including Wolff (2021).
Cyberattacks intensified in the weeks preceding the
current war. The coordination of attacks disrupting
banks’ online services with text message (SMS) disinfor-
mation campaigns, as observed in Ukraine, increases this
risk. Cyberattacks led by private actors have also been
reported against Russian institutions, which may further
escalate tensions on both sides.
Attacks could target systemically important financial
institutions. If successful, such attacks could trigger loss
of confidence in the broader financial system, with a
potentially adverse impact on global financial stability.
Cyber threats against SWIFT and other shared financial
and non-financial market infrastructure could also
increase. Intense hacktivism and false-flag operations
that disguise the actual source of the attack and place
responsibility on another party further complicate the
situation. As cyber risks rise globally, operational costs
have increased across industries, with the potential for
significant economic loss in various countries.
The War and a Repricing of Risk in Markets May Put
Corporate Sector Recovery at Risk
The war in Ukraine has clouded the corporate
outlook. Firms most at risk are those in Russia, which
will suffer trade barriers, lack of intermediate inputs,
and depressed domestic demand. Additionally, more
than 60 percent of Russia’s external debt of close to
$500 billion is owed by nonfinancial firms. Elsewhere,
the impact of heightened uncertainty, sanctions, and
the anticipated slowdown of the economy is evi-
dent especially in Europe due to its greater exposure
to Russia through trade and investments in energy
firms and projects (Figure 1.18, panel 1). European
firms have the largest direct exposures to Russia and
Ukraine, as measured by revenues from the region
(Figure 1.18, panel 2). Sanctions imposed on Russia,
the self-imposed exodus of large firms from Russia,
and a slump in demand in Russia and Ukraine are
expected to result in a sharp decline in global firms’
revenues derived from the region.33 On a sectoral basis,
many large European firms have some exposures to
Russia and Ukraine (above 2 percent of revenues from
the region). However, the share of debt at firms with
33“Over 600 Companies Have Withdrawn from Russia – But
Some Remain,” Yale School of Management (April 12, 2022).
https:// som .yale .edu/ story/ 2022/ over -600 -companies -have
-withdrawn -russia -some -remain.

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23International Monetary Fund | April 2022
substantial exposures (above 5 percent of revenues
from the region) is less than 10 percent of the total
debt of all firms in these sectors. Since the Russian
invasion of Ukraine, most large international com-
panies have announced exits of various types from
Russia because of the reputational risk and the diffi-
culty of doing business in Russia related to sanctions
( Figure 1.18, panel 3).
Global firms have been hit by the rise in energy and
raw material prices. In addition, supply chain chal-
lenges that have emerged during the pandemic have
been exacerbated by the uncertainties and reductions in
export quantities of agricultural commodities, energy,
metals, and technology inputs affecting a variety of
industries. While large firms are generally in a better
position to secure shipments of rationed components
Withdrawal Suspension Scaling back No expansion No change
US
Advanced Europe
Advanced Asia
EMEA
Emerging Asia
Latin America
Exposures between
2% and 5%
Exposures > 5%
2022 revision
2023 revision
Figure 1.18. Corporate Sector amid the War in Ukraine
Uncertainty about the corporate sector outlook has increased,
especially in Europe.
1. Dispersion in Earnings Forecasts
(Index, January 1, 2022 = 100)
90
130
95
100
105
110
115
120
125
0
30
5
10
15
20
25
Among international peers, European firms have the largest exposures
to Russia and Ukraine.
2. Percent of Firms with Exposures to Russia and Ukraine
(Percent of firms with >2 percent exposures in left chart; share of
debt at firms with exposures in total debt of all firms by sector in
right chart)
0
80
20
40
60
CE
E
fir
m
s
Most large international companies have announced exits of various
types from Russia.
3. Number of Large Firms that Have Exited Russia Since Its Invasion
of Ukraine
(Number)
Analysts have slashed earnings forecasts across nearly all major
sectors.
0
200
50
100
150
4. Revisions in Advanced Economy Corporate Earnings Forecasts
(Percent of 2019 earnings, from pre-war period to present)
Sources: Bloomberg Finance L.P.; FactSet; MSCI; Refinitiv Datastream IBES; Yale School of Management; and IMF staff calculations.
Note: Panel 1 presents standard deviations in analyst forecasts of earnings per share over the next 18 months. In panel 2, foreign exposures are defined as revenues
derived from abroad in percent of total revenues. In panel 2, the sample includes 529 CEE firms and 2,079 Japanese firms. CEE = central and eastern Europe and
includes Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Turkey; EMEA = emerging Europe, Middle East, and
Africa.
07
J
an
.
14
J
an
.
21
J
an
.
28
J
an
.
04
F
eb
.
11
F
eb
.
18
F
eb
.
25
F
eb
.
04
M
ar
.
11
M
ar
.
18
M
ar
.
25
M
ar
.
01
A
pr
.
ST
O
XX
E
ur
op
e
60
0
US
S
&
P
50
0
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in
a
S&
P
50
0
Ja
pa
ne
se
fi
rm
s
Co
ns
um
er
c
yc
lic
al
s
En
er
gy
In
du
st
ria
ls
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on
-e
ne
rg
y
m
at
er
ia
ls
Co
ns
um
er
s
er
vi
ce
s
Co
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on
cy
cl
ic
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s
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ea
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c
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na
nc
e
O
th
er
Consumer discretionary
Airlines
Communication
Financials
Information technology
Health care
Food retail
Energy
–8 –4 0 4 8 12 16 20 24 28 32

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24 International Monetary Fund | April 2022
and to pass on the increase in input costs to customers
because of greater pricing power, even before the war
analysts had noted that the pass-through to custom-
ers had become more limited and that profit margins
were expected to shrink. For example, small European
businesses in the transportation and agricultural sectors
have already sounded the alarm about energy prices,
and over half of US small businesses have voiced con-
cerns about energy prices.
So far, analysts have maintained a positive outlook
for most sectors (except airlines), with 2022 earnings
projected to be well above pre-pandemic levels. How-
ever, analysts have started to substantially downgrade
earnings forecasts across sectors, except for energy
(Figure 1.18, panel 4). A prolonged war, an escalation
of sanctions, higher commodity prices, and increased
investor risk aversion could further worsen the corpo-
rate outlook. Energy and agricultural product import-
ers in emerging markets and countries with strong
trade links with Russia and Ukraine have already seen
a more adverse market reaction compared to their
peers, based on equity indices and credit spreads. More
broadly, increased and lingering uncertainty associ-
ated with the war and elevated geopolitical risks are
detrimental to corporate investment at a time when it
is most needed for the transition to a post-pandemic
and greener economy.34 The economic impact of
underinvestment could be especially detrimental for
vulnerable firms that have already built up debt in the
last two years.35 In addition, higher inflation because
of rising commodity prices, wage pressures in some
regions, tighter financial conditions, and a more
cautious lending posture by banks may substantially
affect firms’ revenues and exacerbate funding chal-
lenges for vulnerable businesses, including small and
medium-sized firms.
A repricing of risk by investors—due for exam-
ple to an escalation of the sanctions, a sharper than
previously expected tightening of monetary policy,
or a deterioration of the economic outlook—could
result in a sharp tightening of financial conditions,
a development that could interact with unresolved
pandemic-related vulnerabilities in the corporate
sector. A deterioration in liquidity and funding
conditions could be particularly challenging for risky
credit markets, an important barometer of risk taking.
34For an overview of the literature on investment under uncer-
tainty, see Dixit and Pindyck (1994).
35See Chapter 2 in the April 2022 World Economic Outlook.
Spreads on high-yield bonds and leveraged loans have
widened in advanced economies on the heels of rising
market volatility and implications of higher energy
and labor costs especially for smaller firms—and are
now slightly above pre-pandemic levels. Outflows
have accelerated from high-yield bond funds, and new
issuance has slowed. Issuance has similarly decelerated
in the collateralized loan obligation (CLO) market,
as spreads have increased in both secondary market
leveraged loans and CLO tranches. Should geopolitical
tensions prove longer lasting than currently anticipated
and if economic growth were to slow, risky borrowers
could face tougher financing conditions and higher
rollover risks, potentially resulting in a deeper default
cycle that could severely impact the real economy.
The tightening in market conditions could be ampli-
fied by the deterioration in underwriting standards
and first-lien investor protections seen in recent
years in both the high-yield bond and leveraged loan
market—as reflected by weaker covenants and thinner
loss-absorbing buffers for loans. In addition, tighter
monetary policy comes in the form of higher interest
costs for leveraged loan issuers and could eventually
pressure debt servicing capacity.
Emerging Markets Have Come under Pressure,
with Notable Differences across Countries
Since the Russian invasion of Ukraine, emerging
market hard currency spreads have widened at a rapid
pace, akin to earlier episodes of emerging market
stress, before retracing part of the move in mid-March
( Figure 1.19, panel 1). Credit spreads moved as much
as 113 basis points higher—or 84 basis points exclud-
ing Russia and Ukraine—after the war in Ukraine
started, with a more pronounced widening among
high-yield issuers. Weaker issuers were already under-
performing before the war as the prospect of mon-
etary policy normalization in the United States was
starting to weigh heavily on countries with elevated
post-pandemic vulnerabilities. The number of issuers
trading at distressed levels has surged higher to nearly
25 percent of issuers (Figure 1.19, panel 2), surpassing
pandemic-peak levels. The deterioration in spreads,
combined with the increase in US yields, has pushed
financing costs well above their pre-pandemic levels
for many borrowers (Figure 1.19, panel 3). Emerging
market sovereign issuance has been sluggish in recent
months, with market access for frontier economies

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25International Monetary Fund | April 2022
Share Distressed EMs (spread >1,000 bps)
Median EM 75th percentile of EM index
High yield (sub-investment grade)
High yield (sub-investment grade) Investment grade
Avg. monthly total (previous 3 years)
China
Commodity importers
Commodity exporters
Current (February 2022)
Current (February 2022, ex. RUS/UKR)
February 2020
July 2011
May 2013
Commodity exporters
rated BB– or lower
Commodity importers
rated BB– or lower
Gulf Cooperation Council
LatAm exporters
Figure 1.19. Emerging Market Financial Spillovers
Credit spreads widened sharply as tensions escalated and the war
began before pulling back as risk sentiment stabilized.
1. Emerging Market Sovereign Credit Spreads
(Basis points; change from beginning of each episode)
2. Distressed Sovereign Issuers in Hard Currency
(Number of economies, percent share)
More than 20 percent of issuers have spreads in distressed territory
(above 1,000 basis points).
3. Emerging Markets Sovereign Hard Currency Bond Yields
(Percent)
External funding costs for the weakest borrowers have moved above
pre-pandemic levels.
4. Emerging Markets Sovereign Hard Currency Issuance
(Billions of US dollars)
Hard currency issuance has dried up in recent months and practically
disappeared since the start of the war.
Commodity importers have seen credit spreads widen sharply over a
short period.
5. Emerging Market Sovereign Hard Currency Spreads
(Basis points, cumulative change from Feb. 1, 2022)
6. EM Equity Performance and Commodity Exposure
(Indexed to Jan. 1 = 100, percent change)
Equity markets in commodity exporters have outperformed in 2022.
Sources: Bloomberg Finance L.P.; JPMorgan Chase & Co.; UN Comtrade; and IMF staff calculations.
Note: In panel 1, the current episode is the cumulative change since Feb. 18. In panel 4, yields are calculated from JPMorgan Emerging Market Bond Index. In panel
6, net trade balance is based on oil, wheat, and base metals as share of GDP. In panel 5, BB exporters include Angola, Bahrain, Ecuador, Iraq, Nigeria, and Oman.
Importers include the Dominican Republic, Egypt, Georgia, Jordan, Kenya, Pakistan, Senegal, and Tunisia. EM = emerging market; LatAm = Latin America;
RUS = Russia; UKR = Ukraine.
Jan.
2016
July
16
Jan.
17
July
17
Jan.
18
July
18
Jan.
19
July
19
Jan.
20
July
20
Jan.
21
Jan.
22
July
21
Sep.
2021
Nov.
21
Dec.
21
Jan.
22
Oct.
21
Feb.
22
Mar.
22
0
10
20
30
40
2006 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22
0
50
100
150
200
250
300
350
400
450
3
4
5
6
7
8
9
10
11
12
13
Feb. 1 Feb. 8 Feb. 15 Feb. 22 Mar. 1 Mar. 8 Mar. 15 Mar. 29Mar. 22
0
5
10
15
20
25
30
80
85
90
95
100
105
110
115
120
125
130
Jan. 2022 Feb. 22 Mar. 22 Apr. 22
–50
0
50
100
150
200
250
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26 International Monetary Fund | April 2022
in particular deteriorating. The share of high-yield
issuance had dropped notably since the third quarter of
2021, including a nearly four-week freeze following the
escalation of hostilities. Nigeria and Turkey reopened
the market on March 17, 2022, after risk sentiment
had improved, albeit with a substantial premium over
their existing benchmarks and coupons over 8 percent
(Figure 1.19, panel 4).36
Commodity exposures and trade linkages to Russia
and Ukraine have been a key source of differentiation
in terms of market performance. The role of Russia
and Ukraine in energy, metals, agriculture, and tourism
has exposed several emerging markets to a large deteri-
oration in their terms of trade, upside risks to infla-
tion, and increased pressures on fiscal accounts given
food and energy subsidy policies. Flight-to-quality
dynamics, as well as investor preference toward
countries that are set to benefit from the rise in
commodities, have led to a general outperformance
of higher-rated commodity exporters, both in credit
and equity markets (Figure 1.19, panels 5 and 6). The
differentiation is also notable among lower-rated issu-
ers, where spreads have widened significantly for some
commodity importers.
Portfolio Flows Have Come under Pressure, with High
Differentiation across Economies and Risks Tilted to
the Downside
After a challenging end to 2021 for portfolio flows,
flows into emerging market local currency debt and
equity markets strengthened in early 2022, defying
expectations of policy normalization in the United
States. Fund inflows were stronger for countries
in Asia, eastern Europe, and the Middle East and
North Africa, reflecting subsiding concerns about the
pandemic, and in some cases rising commodity prices
(Figure 1.20, panel 1). Moreover, hiking cycles were
already much farther along in many emerging markets,
creating attractive risk compensation (carry) for inves-
tors in both real and nominal terms when compared
to advanced economies. Finally, the potential for large
outflows was seen as low, as the nonresident investor
base had been considerably reduced in preceding years
(Figure 1.20, panel 2).
36Frontier markets include 42 countries, incorporating 31 coun-
tries from the JP Morgan Next Generation Markets Index.
However, following the Russian invasion of Ukraine,
flows become highly volatile and reversed quickly for
some economies. Flows in local currency bonds and
equities have come under pressure, experiencing the
largest weekly redemptions since March 2020. The first
signs of differentiation across countries have emerged
(Figure 1.20, panel 3). Economies benefiting from
higher commodity prices, such as Brazil and Indonesia,
withstood the pressure and have seen large equity
inflows on net so far this year, while some energy
importers have seen sharp equity outflows. Some of the
outflows in more liquid markets like Chinese sover-
eign bonds (which saw the largest monthly outflow on
record in February) in part reflect technical factors, as
fund managers have reportedly raised cash holdings in
expectation of possible redemption pressure. The need
for short-term liquidity was further amplified by the
highly illiquid market conditions in Russian markets
due to sanctions and trading restrictions.
Looking ahead, the interplay of tighter external
financial conditions on the back of monetary policy
normalization in the United States and heightened
geopolitical uncertainty is likely to increase the down-
side risks for portfolio flows. IMF staff analysis shows
that capital flows at risk (the 5th percentile of the
range of capital flow forecasts to quantify the downside
risks; see IMF 2019 for more details) have increased
to 2.3 percent of GDP from 1.7 percent of GDP
in the October 2021 GFSR, and the probability of
outflows is about 30 percent from 20 percent from the
October 2021 GFSR. A sharp rise of US term premia,
combined with a further rise in risk aversion, would
entail more significant financing risks for emerging
market economies. In such a scenario, these econo-
mies would be subject to much stronger headwinds,
especially countries with lingering inflation risks and/
or elevated debt vulnerabilities. For example, a risk
aversion shock similar to the one seen in March 2020
would take capital flows at risk to 2.5 percent and
increase the probability of outflows to almost 50 per-
cent (Figure 1.20, panel 4).
Risks of Cryptoization and Sanction Evasion through the
Crypto Ecosystem
Crypto asset trading volumes against some emerging
market currencies have increased notably since the start
of the pandemic. Although a large part of this increase
is due to speculative investment activities by emerging

C H A P T E R 1 T h E F I N A N C I A L S T A B I L I T Y I M P L I C A T I O N S O F T h E w A R I N U k R A I N E
27International Monetary Fund | April 2022
market residents, a more structural shift toward crypto
assets as a means of payment and/or store of value
could pose significant challenges to policymakers (see
the October 2021 GFSR for a discussion on cryp-
toization). For example, Tether—the largest stablecoin
used to settle spot and derivative trades—has seen
a notable rise in trading volumes against emerging
market currencies (Figure 1.21, panel 1). The most
pronounced increase is in Turkey, where exchange rate
volatility has been particularly high, and the overall
use of crypto assets appears to have gained traction
over the last few years. More recently, trading volumes
spiked following the introduction of sanctions against
Russia and the use of capital restrictions in Russia and
Ukraine ( Figure 1.21, panel 2).37 However, liquidity
37The spike in trading preceded Ukraine’s enactment of the Law
on Virtual Assets (March 17, 2022), which legalized crypto assets.
Jan. 2022 Feb. 22 Mar. 22 Jan. 2022 Feb. 22 Mar. 22
Hard currency bonds
Equity (China)
Local currency bonds
Equity (EMs excluding
China)EMs excluding Chinese
equities
CEEMEA Asia LatAm
Malaysia Turkey
South Africa Brazil
Philippines Thailand
India Indonesia
China Total
Mexico
Ukraine
South Africa
India
Turkey
Hungary
Thailand
Indonesia
Total
GFSR October 2021 Latest Risk-off scenario
Capital flows
at risk
1.7%
2.3%
2.5%
Figure 1.20. Emerging Market Portfolio Flow Pressures Have Intensified
Portfolio flows recovered in early 2022 but have come under renewed
pressure recently.
1. Fund Flows to Emerging Markets
(Billions of US dollars, two-week moving sum)
–10
20
–5
0
5
10
15
2. Foreign Share of Local Currency Bonds
(Percent)
Foreign holdings of local currency debt have been close to multiyear
lows for several issuers.
35
10
15
20
25
30
3. Local Currency Flows
(Cumulative year to date, billions of US dollars)
Local currency outflows declined sharply, with significant
differentiation, before a partial recovery in late March.
–25
25
–20
–15
–10
–5
0
5
10
15
20
4. Capital Flows-at-Risk
(Density function)
Capital flows-at-risk worsened significantly as a result of the decline in
investor risk sentiment.

0.05
0.30
0.10
0.15
0.20
0.25
–6
10
–4
–2
0
2
4
6
8
Sources: Bloomberg Finance L.P.; JPMorgan Chase & Co.; national data sources; and IMF staff calculations.
Note: In panel 3, total equity flows include Vietnam, Sri Lanka, and United Arab Emirates, which are not shown. In panel 4, the risk-off scenario assumes a global risk
aversion shock with Chicago Board Options Exchange Volatility Index reaching the March 2020 peak levels. CEEMEA = Central and Eastern Europe, Middle East, and
Africa; EMs = emerging markets; GFSR = Global Financial Stability Report ; LatAm = Latin America.
Probability of
outflows
20%
30%
50%
0 1 2 3 4 5–4 –3 –2 –1
Russia invades
Ukraine 2/24
Equity flows Bond flows
2010 2211 12 13 14 15 16 17 18 19 20 21
Ja
n.
2
1
Fe
b.
2
1
M
ar
. 2
1
Ap
r.
2
1
M
ay
2
1
Ju
ne
2
1
Au
g.
2
1
Se
p.
2
1
O
ct
. 2
1
N
ov
. 2
1
D
ec
. 2
1
Fe
b.
2
2
M
ar
. 2
2
Ju
ly
2
1

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
28 International Monetary Fund | April 2022
in the ruble and hryvnia trading pairs in centralized
exchanges remains limited and has even declined more
recently in the case of ruble,38 making large-scale
transfers of value through crypto asset exchanges
impractical.
The war in Ukraine has brought to the forefront
some of the challenges that regulators face in terms of
applying sanctions and capital flow management mea-
sures. Crucially, the implementation of such measures
requires that intermediaries verify the identities of the
transacting parties. The crypto ecosystem, however,
could allow users to circumvent such requirements
through several means, including (1) the use of
exchanges and other crypto asset providers that are non-
compliant with sanctions and/or capital flow manage-
ment measures; (2) poor implementation of adequate
due diligence procedures by crypto asset providers; and
(3) the use of technologies and platforms that increase
38Major exchanges have frozen the accounts of sanctioned entities,
while new ruble deposits in exchanges may have been blocked (see
Binance 2022). As a result, part of the transaction volumes could
have shifted to less transparent peer-to-peer platforms.
the anonymity of transactions (such as mixers, decen-
tralized exchanges, and privacy coins).39 Regulators in
the United States and United Kingdom, among others,
have urged firms in their jurisdictions, including the
crypto asset sector, to increase vigilance with regard to
potential Russian sanction evasion attempts.40
Over time, sanctioned countries could also allocate
more resources toward evading sanctions through
mining. Mining for energy-intensive blockchains
like Bitcoin can allow countries to monetize energy
resources, some of which cannot be exported due
to sanctions. The monetization happens directly on
blockchains and outside the financial system where the
sanctions are implemented. Miners can also generate
39Chainalysis (2022) has reviewed several potential sanction eva-
sion mechanisms since the start of the war. None of the indicators
showed a sustained spike in volumes at the time of writing.
40For the United States, see “FinCEN Advises Increased Vigilance
for Potential Russian Sanctions Evasion Attempts,” U.S. Financial
Crimes Enforcement Network Fin-2022-Alert001 (March 7, 2022);
for the United Kingdom, see the “Joint Statement from UK Finan-
cial Regulatory Authorities on Sanctions and the Cryptoasset Sector,”
Financial Conduct Authority (November 2, 2021).
RUB vs. Tether UAH vs. Tether
Turkey (lira) Ukraine (hryvnia) Russia (ruble)
Brazil (real) Nigeria (naira) US (dollar)
1. Tether Trading Volumes against Select Currencies
(Share of total, percent)
2. Tether Trading Volumes
(Millions of US dollars)
The share of Tether volumes against EM currencies has been rising
since the pandemic began.
The ruble and hryvnia have seen a spike in crypto trading volumes in
centralized exchanges.
Sources: Bloomberg Finance L.P., CryptoCompare; and IMF staff calculations.
Note: AE = advanced economies; EM = emerging markets; RUB = Russian ruble; UAH = Ukrainian hryvnia.
Figure 1.21. Crypto Asset Markets
0
20
40
60
80
100
Jan.
2020
Apr.
20
July
20
Oct.
20
Jan.
21
Apr.
21
July
21
Jan.
22
Oct.
21
Dec.
2020
Mar.
21
June
21
Sep.
21
Dec.
21
Mar.
22
Dec.
2020
Mar.
21
June
21
Sep.
21
Dec.
21
Mar.
22
0
10
20
30
40
0
5
10
15
20
25
30
35
40
Start of the
war in Ukraine
Start of the
war in Ukraine

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29International Monetary Fund | April 2022
revenues directly from users that pay transaction fees to
miners (which in this case might be sanctioned govern-
ments). At this point, the share of mining in countries
under sanctions and the overall size of mining revenues
suggests that the magnitude of such flows is relatively
contained, although risks to financial integrity remain.
For example, the monthly average of all Bitcoin
mining revenues last year was about $1.4 billion, of
which Russian miners could have captured close to
11 percent, and Iranian miners, 3 percent.41
Financial Vulnerabilities Remain Elevated in
China amid Ongoing Stress in the Property
Development Sector and COVID-19 Risks
Concerns about a sharper-than-anticipated growth
deceleration in China amid elevated financial vulnera-
bilities have weighed on the global economic outlook.
Chinese equity prices have slumped, particularly in the
tech sector, amid new outbreaks of COVID-19 and
worsening investor sentiment, in part reflecting the
impact of continued regulatory uncertainty and rising
geopolitical risks. Financial stability risks have risen
amid ongoing stress in the battered real estate sector, a
major source of China’s economic growth and house-
hold wealth in the past decade. Severe financing strains
have spread through much of the property develop-
ment sector, generating spillovers to housing sales, real
estate investment, and land sales. Widening mobility
restrictions aimed at containing COVID-19 outbreaks
could delay recovery in the property market and pose
further disruptions to spending and income. Excep-
tional financial support measures may be necessary to
ease balance sheet pressures but would add further to
medium-term debt vulnerabilities.
Credit availability has deteriorated for some corpo-
rate borrowers, notably home builders, whose offshore
US dollar bonds have slumped by more than 50 percent
since the second half of 2021.42 Amid property market
pressures and signs of slowing growth, Chinese author-
ities have taken steps to ease property sector financing
controls, lower policy interest rates, and increase fiscal
spending. Authorities have also pledged to stabilize
financial markets and reduce regulatory uncertainty for
tech firms, supporting investor sentiment.
41These figures are as of August 2021 and are based on the
Cambridge Bitcoin Electricity Consumption index.
42Property developers have nearly $215 billion in debt outstand-
ing in offshore US dollar bond markets.
Financial stress in the developer sector has neverthe-
less worsened amid evidence of self-reinforcing pres-
sures on liquidity, creating risks of broader spillovers to
the housing market, financial sector, and the real econ-
omy. Property developers have relied heavily on presales
of unfinished properties as a key source of funding.
Amid concerns that developer balance sheet problems
may affect their capacity to finish presold homes, home
purchases have slowed sharply, and local governments
have tightened escrow requirements to ensure sufficient
funds to complete local projects. These factors have
exacerbated the large liquidity gap created by contrac-
tual spending commitments, which had typically been
covered by additional borrowing and new presales
(Figure 1.22, panel 1). These liquidity pressures, along
with news that many developers carried substantial
hidden debts or guarantee obligations on top of their
already thinning equity buffers, have reinforced a sharp
tightening in credit availability for the sector.
Disruptions to the completion of presold housing
could reinforce market pressures on real estate firms
and the broader housing market. Property develop-
ers’ large stock of presold but unfinished housing
has grown rapidly and is nearly equivalent to the
size of all private housing completed since 2015
(Figure 1.22, panel 2, left side). Financial statements
show that nearly half of presale liabilities are owed by
“developers-at-risk,” defined as those with liquidity
shortfalls (Figure 1.22, panel 2, right side, sum of
orange and gray bars).43 Unfinished housing projects
could affect property prices for adjacent developments
and weigh on valuations of property developers’ inven-
tories, raising solvency concerns.
Financial strains in the property development sector
could create several mutually reinforcing channels of
macro-financial stress.44 First, prolonged dislocations
in new home sales could trigger a correction in prop-
erty prices due to high valuations and oversupply in
some cities. Prices appear stretched across the coun-
try.45 Inventory overhangs are also significant in some
of China’s smaller Tier 2 cities outside the eastern
43Liquidity shortfalls are defined as cash being less than combined
net current liabilities, net interest payment, and contractual capital
commitments.
44Worsening property sector stress could create international
spillovers, see IMF (2022, Box 4).
45Price-to-income ratios in China’s smaller and less developed
Tier 2 and Tier 3 cities are about twice those of the five largest
advanced economy cities, and those in China’s larger and wealthier
Tier 1 cities are closer to four times higher.

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
30 International Monetary Fund | April 2022
provinces and in less developed Tier 3 cities. Large
declines in house prices could also reinforce tightening
financial conditions through balance sheet channels, as a
large share of loans are collateralized by real estate assets.
Second, property developers’ financial strains are likely
to add to the fiscal pressures of local governments, con-
straining financing conditions for some vulnerable firms
dependent on local authorities’ support. Provincial or city
authorities may have to pick up the cost of completing
unfinished housing projects to avoid further destabilizing
homebuyer confidence in housing markets. Land sales,
which account for a sizable share of local governments’
gross funding, are also falling sharply as liquidity-strapped
property developers pull back on purchases. In provinces
with weak public finances, deepening investor concerns
about the credibility of local governments’ backstops
for local firms could exacerbate an existing pullback in
corporate credit availability (Figure 1.23, panel 1) or
precipitate the default of a local government financing
vehicle (see the October 2021 GFSR).
Finally, rising defaults by property developers could
impair balance sheets across the broader private sector,
weighing on credit intermediation and aggregate
demand. Aggregated total liabilities of property devel-
opers with publicly available data are nearly 25 percent
of GDP, with roughly half of that attributable to those
with liquidity shortfalls (defined as “liabilities-at-risk”).
Roughly half of these liabilities-at-risk, or about 6 per-
cent of GDP, are owed to business partners and home-
buyers, with the other half owed to financial institutions
(Figure 1.23, panel 2). Rising balance sheet stress
across banks and private borrowers alike could limit
banks’ capacity and willingness to extend new credit,
weakening growth momentum. As property developers’
liquidity worsens, mortgage credit availability could also
suffer as banks rely on property developers’ guarantees
to provide mortgages against presold homes.
Selected Medium-Term Structural Challenges
Policymakers Will Need to Confront
Could the Geopolitics of Energy Security Put the Energy
Transition and thus Financial Stability at Risk?
The Russian invasion of Ukraine, the ensuing
sanctions, and the actions of market participants in
response to a global outcry have wreaked havoc in
Cash
Net current liabilities
Estimated net interest expense
Contracted capital commitment
Common equity
Estimated increase in cash escrow requirements
Adjusted cash
Leverage Liquidity
Pre-sold but unfinished
housing and completed
housing, in cumulative gross
floor area since 2015
(billions of square meters):
Liabilities from pre-sold but
unfinished housing, by
developer liquidity risk level
(percent of GDP):
Developers-at-risk
including estimated
impact of tighter
escrow rulesCompleted
housing
Developers-at-risk
Pre-sold but
unfinished housing
Credit contagion reflects leverage concerns and liquidity shocks from
tighter escrow requirements.
Liquidity stress may affect completion of a large stock of pre-sold but
unfinished housing.
Figure 1.22. Stress in the Chinese Property Development Sector
1. Real Estate Firm Leverage and Liquidity
(Percent of total assets)
2. China: Cumulative Housing Completions and Developer Balance
Sheet Inventories
(Billions of square meters; percent of GDP)
Sources: Bloomberg Finance L.P.; CEIC; S&P Capital IQ; and IMF staff calculations.
Note: In panel 1, the estimated increase in cash escrow requirements is calculated as the lesser of 20 percent of unearned revenues or 40 percent of unearned
revenues less restricted cash. In panel 2, developers considered at risk have insufficient cash to cover net current liabilities (including net interest payments and
contracted capital commitments) or net current liabilities and an estimated increase in cash escrow requirements as calculated in panel 1. Data for 2021 are from
end-June.
–35
–25
–15
–5
5
15
25
0
2
4
6
8
10
12
0
1
2
3
4
5
6
2015 2021:Q2 2015 2021:Q2 2015 17 19 21 15 17 19 21
Other developers

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31International Monetary Fund | April 2022
commodity markets. Disruptions in supply chains,
rising concerns about counterparty risk, and grow-
ing worries about energy availability have pushed
commodity prices higher across the entire com-
plex (Figure 1.2). Given Russia’s large footprint in
global commodity production, not only oil and
gas prices, but also widely used metals (including
those used for renewables), have increased sharply
( Figure 1.24, panel 1).
Against this backdrop, the war in Ukraine has crys-
talized concerns about energy security across the globe.
With the perception of the trade-off between energy
security and transition changing rapidly, there is a risk
that the transition toward renewables may become
more costly, complex, and disorderly. Given that
climate change poses a threat to financial stability, a
delayed and disorderly climate transition may mag-
nify risks to the financial system. There may be some
setbacks in the immediate future, but the impetus
to reduce energy dependency on Russia could be a
catalyst for change. It is therefore crucial that policy-
makers intensify their efforts to achieve net-zero targets
and lever up private finance to accelerate the transition
toward a greener economy.
The war has indeed made evident the energy
dependency of Europe on Russia. In particular,
Europe relies on Russia for roughly 40 percent of
its consumption of natural gas and for more than
50 percent of thermal coal, (Figure 1.24, panel 2).
Renewable energy currently accounts for only 22 per-
cent of energy consumption in Europe. In response to
the war, Europe is rethinking its energy landscape (for
example, through the REPower EU agenda).46 How-
ever, uncertainties remain in the short term. Physical
bottlenecks are significant, for example in the context
of switching to coal-fired power generation. In addi-
tion, Europe’s diversification strategy (with increased
46REPower EU is a multifaceted plan announced in early March
2022 by the European Commission that aims to reduce gas imports
from Russia by almost 70 percent by the end of this year, refill-
ing gas storage, increasing investment in regasification terminals,
and speeding up the transition with supply- and demand-driven
measures. The statement by the European Commission and the
United States on energy security, published on March 25, 2022,
which builds on the REPower EU agenda, aims at terminating EU
dependency on Russian gas by 2027. Germany’s Federal Ministry
for Economic Affairs and Climate Action on March 25, 2022 also
announced plans to fully move away from Russian gas imports by
the end of 2024.
Fifth quintile (lowest LG debt to GDP)
Fourth
Third
Second
First quintile (highest LG debt to GDP)
Other
liabilities
Other debt
Bank debt
Bond debt
Accounts
payable and
accrued
expense
Unearned
revenue
Remaining
liabilities
Liabilities-at-risk
Rising macro-fiscal pressures may exacerbate challenging credit
conditions for firms in provinces with heavier debt loads.
Rising defaults could spill over to bank loan books and other private
sector balance sheets.
Figure 1.23. Chinese Property Development Spillovers
1. Growth in Outstanding Corporate Bonds
(Percent; by quintile of home province government debt)
2. Liabilities and Financing of Real Estate Firms
(Percent of GDP)
Sources: Bloomberg Finance L.P.; CEIC; S&P Capital IQ; WIND Information Co.; and IMF staff calculations.
Note: In panel 2, data are from mid-2021 or latest available. Banks’ exposures to real estate firms include their direct lending to real estate firms and their mortgage
lending to homebuyers; the latter, which is guaranteed by real estate firms, is for financing unfinished, presold housing. LG = local government.
0
5
10
15
20
25
Mar.
2017
Sep.
17
Mar.
18
Sep.
18
Mar.
19
Sep.
19
Mar.
21
Mar.
20
Sep.
21
Sep.
20
Total
liabilities
Bond
creditors
Other
creditors
Banks Home
buyers
Business
partners
–20
–10
0
10
20
30
40

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
32 International Monetary Fund | April 2022
imports from the Asia, Australia, and the United
States) is likely to take time to be fully implemented
amid rising global energy demand (especially in Asia)
and supply constraints.
The war has also made evident the urgency to
cut dependency on carbon-intensive energy and
accelerate the transition to renewables. However,
the energy transition strategy may face setbacks for
some time. Some countries have already indicated
their intention to switch to domestic coal-fired
power generation and fossil fuel production to secure
their energy needs in the short term. Moreover, the
current energy crisis is likely to weigh on the speed
of phasing out fossil fuel subsidies in emerging
market and developing economies and could also
delay the decommissioning plans for coal-fired power
plants—especially in major coal-exporting countries
(Australia, Indonesia, South Africa, United States).
Rising inflation pressure may also lead authorities to
resort to subsidies or other forms of fiscal support to
households or firms, with the risk of delaying climate
transition plans.
Clean vs. coal
Clean vs. oil and gas
Coal Gas Palladium
Net zero by 2050 (additional capacity)
Accelerated case (additional capacity)
Main case
Actual
Share in production
Price change between Feb. 23 and Mar. 23, 2022 (right scale)
Figure 1.24. The War in Ukraine Tests the Climate Challenge
Commodity prices have jumped across the entire complex given
Russia’s substantial share of the world’s energy supply …
1. Russia’s Share in Global Production and Price Change since the
Start of the War
(Percent)
… leading to decisive trade-offs in the short to medium term due to
Europe’s reliance on Russia for key commodities.
2. Share of Russia in Respective Import Volumes in the European Union
(Percent)
Recent outperformance by renewable energy indices has deteriorated
amid energy security concerns …
3. Relative Performance of Clean Energy Exchange-Traded Funds vs.
(Thermal) Coal and Oil and Gas Index
(Ratio)
… as Europe’s reliance on Russia for key commodities is leading to
decisive trade-offs in energy policy in the short to medium term.
4. Evolution in Renewable Energy Capacity and Forecasts in a
Net-Zero Scenario
(Total capacity in gigawatt)
Sources: Bloomberg Finance L.P.; BP Statistical Review of World Energy; International Energy Agency; UN Comtrade; US Geological Survey, National Minerals
Information Center; and IMF staff calculations.
Note: In panel 4, IEA’s forecasts are shown for 2026, where main case is the base case scenario, accelerated case is a more optimistic scenario, and Net-zero by
2050 case estimates capacity needed to transition to a net-zero energy system by 2050.
Ap
r.
1
6
Ap
r.
2
1
Au
g.
1
6
Au
g.
2
0
N
ov
. 1
9
Ju
ly
1
9
Fe
b.
1
9
O
ct
. 1
8
Ju
ne
1
8
Ja
n.
1
8
M
ay
1
7
D
ec
. 2
0
D
ec
. 2
01
5
D
ec
. 1
6
Ja
n.
2
2
M
ar
. 2
0
Se
p.
2
1
Se
p.
1
7
0
3
6
9
12
15
18
–10
0
10
20
30
40
2005 2010 2015 2021
0
10
20
30
40
50
60
0
2
4
6
8
10
Aluminum Copper Nickel Platinum Coal Oil Gas
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
2010 11 12 13 14 15 16 17 18 19 20 21 26

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33International Monetary Fund | April 2022
In addition, the buildup of renewable energy
infrastructure will require time and is likely to face
headwinds amid rising prices and supply disruptions
of critical commodities (such as cobalt, palladium,
and nickel). As an indication of possible headwinds,
the increased focus on energy security appears to have
adversely affected the performance of clean energy
indices relative to fossil fuels. This weaker performance
has occurred despite strong investor demand for
low-carbon assets and a substantial decline in renew-
able energy costs in recent years (Figure 1.24, panel 3).
Meanwhile, renewable energy supply remains limited
amid a shortfall in renewable energy investment,
( Figure 1.24, panel 4).
The most recent Intergovernmental Panel on Cli-
mate Change report has highlighted that fossil-fuel
burning is “choking humanity,” enhancing the urgency
of the energy transition to avoid carbon lock-in in
infrastructure and policy, and therefore irreparable
damage to our planet. Meanwhile, the war in Ukraine
has brought to fore the need to ensure energy security
and the mitigation of supply vulnerabilities in a world
where the geopolitical landscape is rapidly changing.
Policymakers need to strike the appropriate balance to
achieve fundamental objectives that may at times seem
difficult to reconcile.
As the Line between Geopolitics and Financial Markets
Gets Blurred, New Challenges Arise
The swift imposition of sanctions and the immo-
bilization of the assets of the Central Bank of Russia
have raised a number of issues that policymakers must
confront. One key issue is whether the composition
of exchange rate reserves will change. Some market
commentators have argued that reserve managers may
opt to diversify away from currencies of advanced
economies and the US dollar in particular. Potential
beneficiaries of such a shift may be assets that the
Group of Seven (G7) will find more difficult to immo-
bilize if there are new geopolitical events, including the
Chinese renminbi, commodities, and potentially even
crypto assets.
For now, such a scenario appears distant. The
composition of currencies held by central banks has
remained largely steady over decades. Reserve com-
positional changes can be described as glacial in pace
even considering the small decline of the US dollar
share over the years (Iancu and others 2020). In the
medium to long term, however, geopolitical shifts and
technological changes can indeed cause central banks
to rethink what constitutes, and how to hold, reserves.
Emerging market and developing economies could also
issue more debt in the currencies of emerging credi-
tors, such as China, to help meet increased financing
needs. Countries may become more interested in
ensuring critical supplies that could alter trade links
and invoicing practices. In addition, a shift toward
localized production would reduce the demand for
international currencies. Finally, demand for alterna-
tive reserve currencies may increase in some regions.
Issuers of alternative reserve currencies could increase
the attractiveness of their currencies through leveraging
digital technology, which could help them overcome
some of the advantages of incumbent currencies.
There are strong welfare effects of sharing common
payment infrastructures or critical service providers,
although risks of single points of failure must also be
managed in order to uphold operational resilience.
Costs can be shared, and economies of scale applied.
Likewise, such sharing increases compatibility between
domestic payment systems, which facilitates interna-
tional trade and finance. There is a risk that measures
to increase a country’s resilience to sanctions could
promote the development of parallel national or
regional infrastructures or critical service providers. For
instance, there are currently only a few international
payment message providers other than SWIFT, but
these are generally small and cover a limited geograph-
ical area. Users of the Chinese payment system CIPS,
for instance, currently still rely partly on SWIFT. An
increased ambition to allow for payment messaging
outside of SWIFT could, however, lead to establishing
larger and fully independent and parallel systems. Con-
sequent loss of efficiency and cross-border payment
compatibility could also undermine efforts to improve
access globally to cheap, safe, and efficient cross-border
payments. In particular, there is ongoing international
collaboration to increase compatibility and improve
cross-border payments undertaken under the aegis of
the Group of Twenty (G20) (FSB 2020).
This fragmentation could also arise in emerg-
ing payment infrastructures. Many countries are
currently exploring central bank digital currencies
(CBDCs) and are also looking into their use for
cross-border payments. Within the G20 initiative
to enhance cross-border payments there is a work-
stream on how CBDCs could improve cross-border

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
34 International Monetary Fund | April 2022
payments and increase global economic integration.
Efforts to increase resilience to sanctions could
undermine this project, and instead lead to frag-
mentation as national central banks seek to establish
CBDCs independent of international infrastruc-
tures. There is a risk of competing “CBDC blocs”
with fragmentation across technology and design.
Cross-border compatibility could work well within
the bloc but have little or no compatibility with
CBDCs outside of each bloc.
Finally, the imposition of unprecedented financial
sanctions could also lead to more complex, bespoke,
and less passive asset allocation on behalf of inves-
tors. For example, going forward investors could
place greater importance in their portfolio decisions
on some of the risk factors exposed by the war in
Ukraine (such as currency convertibility, sanctions, and
reputation risk) and less importance on the decisions
of benchmark providers. Analysts have also noted
the possibility of creating bespoke indices that could
cater to the unique mandates of different investors. In
such a scenario, markets that have a higher share of
benchmark-driven investors, including some frontier
economies (IMF 2019), could be especially at risk of
losing portfolio inflows.
Policy Recommendations
Central banks face a challenging trade-off between
fighting persistent inflation and safeguarding the recov-
ery at a time of heightened uncertainty about the global
economic outlook while avoiding a disorderly tightening of
global financial conditions. Higher policy interest rates
and the unwinding of pandemic-related balance sheet
policies will eventually lead to tighter financial condi-
tions. Such a tightening is, in fact, an intended objec-
tive of policy, necessary to slow aggregate demand.
With inflation expected to remain stubbornly high and
significantly above target in many advanced economies,
central banks should act decisively to prevent infla-
tion pressure from becoming entrenched and avoid
an unmooring of inflation expectations. As the war in
Ukraine continues to unfold, the surge in commodity
prices and disruptions to global supply chains pose fur-
ther upside risks to the inflation outlook. Amid tight
labor markets and still robust demand, there is a risk
that wage and price increases may become entrenched.
Against this backdrop, central banks in advanced
economies will need to normalize the monetary policy
stance at a faster pace than was anticipated only a few
months ago to bring inflation credibly back to target.
Policymakers should provide clear guidance about
the policy normalization process while remaining data
dependent. Amid persistent inflation pressure, central
banks face challenges to meet their mandates and
should be resolute in preventing any perceived damage
to their credibility. To avoid unnecessary volatility
in financial markets, it is crucial that central banks
in advanced economies provide clear guidance about
the normalization process. Such guidance should
include both the expected path of policy rates and
the anticipated unwinding of pandemic-related asset
purchases. With significant accommodation still in
place (as evidenced by still meaningfully negative real
rates in many advanced economies), policymakers may
consider a faster pace of balance sheet normalization to
achieve the desired tightening of financial conditions.
Finally, it is also important that the normalization pro-
cess remain data-dependent and be recalibrated along
the way as dictated by the evolution of the economic
and inflation outlook as well as by market conditions
that are already affected by the war in Ukraine.
Emerging market economies remain vulnerable to a
tightening of global financial conditions. While there is
still heterogeneity across emerging markets in terms
of the inflation outlook and policy responses, many
central banks have already significantly tightened
policy, most notably in Latin America and eastern
Europe. Further rate increases, or policy normalization
with respect to other measures such as asset purchases,
should continue as warranted based on country-specific
inflation and economic outlooks and the persistence of
commodity price increases to anchor inflation expec-
tations and preserve policy credibility. In countries
where inflation has surprised on the upside and there
are tangible risks of more persistent price pressures that
put central bank credibility at risk, a more frontloaded
and decisive monetary policy response is needed. An
abrupt and rapid increase in US rates could lead to
significant spillovers to some emerging and frontier
markets, adversely affecting the recovery and further
widening the gap with advanced economies. A disor-
derly tightening of global financial conditions would
be particularly challenging for countries with high
financial vulnerabilities, unresolved pandemic-related
challenges, and significant external financing needs.
Policymakers should take targeted actions to contain
the buildup of financial vulnerabilities during the policy

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35International Monetary Fund | April 2022
normalization process. This includes tightening selected
macroprudential tools to tackle pockets of elevated
vulnerabilities while avoiding a disorderly tightening of
financial conditions. If such tools are not available—
for example, in the nonbank financial intermediation
sector—policymakers should urgently develop them.
Striking a balance between containing the buildup
of vulnerabilities and avoiding procyclicality appears
important in light of persisting uncertainties about
the economic outlook owing to the war in Ukraine,
the ongoing monetary policy normalization process,
and limits on fiscal space in the aftermath of the
COVID-19 pandemic.
On the fiscal front, amid heightened uncertainty and
marked divergence across countries, tailored and agile
fiscal policy response to an evolving situation is war-
ranted (see the April 2022 Fiscal Monitor). In those
economies hardest hit by the war, fiscal policy will
need to address the humanitarian crisis and economic
disruption. Given rising inflation and interest rates,
fiscal support should be targeted to those most affected
and to priority areas. In many emerging markets and
low-income economies, higher inflation and tight-
ening global financial conditions call for prudence,
while fiscal support is needed for those that will be the
hardest hit by the higher commodity prices and where
the recovery was already weaker. To help alleviate the
burden of higher food and energy prices, governments
should provide targeted, temporary, and direct support
to vulnerable households, while allowing domestic
prices to adjust.
While taking steps to address energy security concerns
raised by the war in Ukraine, policymakers should inten-
sify efforts to implement the 2021 United Nations Cli-
mate Change Conference (COP26) roadmap to achieve
net-zero targets. Amid widespread upward pressures on
commodity prices, policymakers should take steps to
increase the availability and lower the cost of fossil fuel
alternatives and renewables while improving energy
efficiency. Authorities should also focus on policies
aimed at scaling up private finance in the transition
to a greener economy to steer the mobilization of
investment and the alignment of capital flows on a
low-carbon trajectory. Toward this end, strengthening
the climate finance information architecture remains
paramount to enhance the development of climate
transition financial instruments and shareholder
engagement practices. This includes improving the
availability of high-quality, consistent, and comparable
climate-related data; developing science-based classifi-
cations for climate finance to align capital flows with
net-zero goals; and implementing global climate-related
disclosure standards that involve transition plans.
Policy Recommendations to Address Specific
Financial Stability Risks
The deterioration in the economic outlook and the
withdrawal of monetary accommodation and other policy
support measures may pressure bank asset quality, so
supervisory authorities should ensure that asset classifi-
cations and loan-loss provisions accurately reflect credit
risk and losses. Any significant decline in capital ratios
should be accompanied by a credible capital restoration
plan. Authorities should also determine whether finan-
cial institutions have a comprehensive risk manage-
ment process, with a special focus on credit, market,
and counterparty risks. Authorities should ensure that
broker dealers have appropriate visibility and buffers
for aggregate derivatives exposures, including adequate
capital and margin requirements for derivatives that are
not centrally cleared.
The surge in volatility and (associated) dislocations in
commodity markets underscore the importance of ensuring
the adequacy of disclosures and standards of transparency
to counterparties, especially major financial institutions
such as dealer banks. These institutions are exposed
to commodity markets through provision of funding
and risk-hedging services. Adequate disclosures and
transparency standards are essential to supporting
comprehensive and strong risk management within
the financial sector and its oversight by supervisory
authorities. Robust risk management at these financial
institutions is paramount, particularly the adequacy
of margining and stress testing vis-à-vis concentration,
market, and credit risks.
While margin calls appear to have been generally orderly
and not disruptive to market functioning so far, recent
measures taken in markets and exchanges in response to
elevated volatility in commodity prices highlight the need
to examine the broader implications of such efforts. For
example, commodity markets function differently than
securities markets, and trading disruptions could exert
significant adverse impacts on the real sector. Exchanges
and central counterparty clearing houses should also
ensure the robustness and resilience of their informa-
tion technology systems to withstand current trading
conditions. Governance mechanisms for the LME

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36 International Monetary Fund | April 2022
need to be strengthened to address conflict of interest.
Measures must be in place to ensure that the concen-
tration of trading does not adversely impact free and
fair markets. Supervisors and regulators should consider
enhancing transparency, in both exchange-traded and
over-the-counter markets, to preempt the buildup
of concentrated positions and thereby limit financial
stability implications.
Recent developments related in particular to the nickel
market on the London Metal Exchange (LME) suggest
that there are a number of potential lessons for policy-
makers to consider.47 While the stated objective of the
cancellation of trades by the LME was to stabilize the
nickel market, counterparties with long positions were
put at a disadvantage. Reportedly, large commodity
traders have voiced concerns over the longer-term
impact of the cancellation and price change limits
on market confidence and participation. This risks a
migration of exchange-traded contracts into uncleared
over-the-counter derivatives, which are more opaque
and do not have the same mechanisms for mitigat-
ing counterparty risks. Disruptions in commodity
derivative markets are particularly problematic at the
current juncture of volatile prices and supply bottle-
necks. Broadly speaking, a disruption in trading needs
to balance financial stability and free and fair market
objectives; the adequacy of governance mechanisms
of market infrastructure institutions requires careful
review from the perspective of mitigating conflict
of interest; and further assessment may be required
concerning the need to enhance transparency in
exchange-traded and over-the-counter markets to
improve the technical soundness of exchange platforms
and avoid concentration of trading (with its implica-
tions on fair trade).
The recent escalation of geopolitical tensions and their
ramifications in the cyber domain have highlighted the
importance of incorporating cyber risk into financial
stability analysis. It is paramount to ensure that cyber
regulation and supervision are fit for purpose and that
response and recovery capacity is improved to ensure
operations can quickly resume if an attack occurs.
Enhancing information-sharing and incident reporting
frameworks and helping emerging market economies
build cybersecurity capacity are key to ensuring that
all nodes of the network are resilient. Stepping up
47On April 4, 2022, UK regulators announced a review of the
LME’s approach to managing the suspension and resumption of the
market in nickel.
international efforts to prevent and deter attackers
would reduce the threat at its source. Addressing all
these gaps requires a comprehensive international
collaborative effort.
Policymakers need a multifaceted policy strategy to
preserve the effectiveness of capital flow management
measures in an environment of increasing use of crypto
assets (see He and others, forthcoming). Essential steps
include developing a comprehensive, consistent, and
coordinated regulatory approach to crypto assets,48 and
applying it effectively to capital flow management mea-
sures; establishing international collaborative arrange-
ments for implementation; addressing data gaps;
and leveraging technology (“regtech” and “suptech”).
Implementation of the existing Financial Action Task
Force standards is key to mitigating financial integrity
risks that might give rise to illicit capital flows. Finally,
laws and regulations for foreign exchange and capital
flow management measures should be reviewed and
amended if necessary to cover crypto assets even if they
are not classified as financial assets or foreign currency.
Policymakers need to urgently develop appropriate mac-
roprudential tools to address risks from nonbank financial
intermediation (NBFIs). Nonbanks play an increas-
ingly important role in the financial system, including
intermediating cross-border capital flows. It is essential
that risks from NBFIs are effectively managed and
that authorities have the right tools to supervise and
regulate NBFIs. The IMF continues to work closely
with the Financial Stability Board and standard setting
bodies to develop these tools.
To fend off cryptoization risks, strengthening macroeco-
nomic policies is necessary but may not be sufficient given
the unique challenges posed by the crypto ecosystem. A
broader discussion of policy recommendations can be
found in the October 2021 GFSR and He and others
(forthcoming). Central bank digital currencies may
also help reduce cryptoization pressures driven by a
need for better payment technologies.
The international community should work to prevent fur-
ther fragmentation of the global payment system. Fragmen-
tation would lead to reduced efficiency of international
payments, with subsequent efficiency loss and fragmen-
tation for trade and finance. Continued and deepened
international cooperation is necessary to achieve this. The
IMF can be an important facilitator of this cooperation.
48The elements of such an approach are further discussed in Bains
and Sugimoto (forthcoming).

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37International Monetary Fund | April 2022
Authorities in emerging and frontier markets need
to safeguard against risks related to tighter external
financial conditions. Countries with stronger fiscal
positions and clearer policy frameworks will be better
positioned to manage tighter conditions. There is a
need to rebuild fiscal policy space and retire extraor-
dinary crisis measures where possible, especially in
some commodity-exporting economies that have
seen an improvement in terms of trade and experi-
enced positive growth surprises. Given the significant
volatility in financial markets since the start of the
war in Ukraine, appropriate use of foreign exchange
intervention measures may be needed, as long as they
do not prevent credible macroeconomic policies and
necessary adjustments. In addition to the warranted
macroeconomic adjustment, in cases of crises or
imminent crises, capital flow management measures
may be an option for some countries to limit outflow
pressures. For weaker sovereign borrowers, enhanced
efforts to contain the risks from high debt and weak
recovery should continue, including via multilateral
cooperation and decisive support from the interna-
tional community.
Some firms and sectors may need short-term fiscal sup-
port to navigate the consequences of the war in Ukraine.
The corporate sector outlook has deteriorated since
the Russian invasion of Ukraine, including as a result
of the surge of energy and raw material prices, adding
to the preexisting vulnerabilities from the pandemic.
While corporate balance sheets have continued to
strengthen, benefiting from unprecedented policy sup-
port and the ongoing economic recovery, smaller firms
may be less resilient and more exposed to a tightening
in financial conditions and a more stringent lending
posture by banks. Solvency risk has remained elevated
for small firms in some countries. Direct government
support to firms may be needed to prevent the risk of
a wave of bankruptcies. Such support should depend
on firms’ viability49 and available fiscal space and be
limited to circumstances in which there was clear mar-
ket failure.50 It is crucial that policymakers continue to
undertake structural measures, including strengthening
insolvency frameworks via a fast-track process.
Amid heightened uncertainty, financial stability risks
stemming from risky credit markets should be mitigated.
Supervisors should take a comprehensive view of risks,
intensify monitoring, and enforce sound underwriting
standards and risk management practices at banks and
non-bank financial intermediaries active in these seg-
ments. Supervisors should ensure that more comprehen-
sive stress tests—incorporating macro-financial feedback
effects from high corporate sector indebtedness, as well as
correlated risks in related sectors (such as commercial real
estate)—are conducted for banks and non-bank financial
intermediaries with significant corporate exposures.
49See the corporate framework, including the operationalization of
viability, in Chapter 1 of the April 2021 GFSR.
50See Chapter 1 of the April 2022 Fiscal Monitor.

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38 International Monetary Fund | April 2022
The London Metal Exchange (LME) suspended
trading in the nickel market for six trading days after
the three-month nickel forward price skyrocketed
on March 8, 2022 (Figure 1.1.1, panel 1). Given
that Russia is the world’s third largest producer of
nickel, nickel prices had been on the rise since the
start of the Russian invasion of Ukraine. Report-
edly, one of the world’s largest nickel producers,
Tsingshan Holding Group, had large short futures
positions (approximately 150,000 tons, of which
about 30,000 tons were on the LME and the rest
were bilateral over-the-counter [OTC] exposures with
various banks). Commodity producers typically hedge
against price declines (yellow line in Figure 1.1.1,
panel 1). As prices increased rapidly (black line), the
Tsingshan Holding Group was apparently unable to
post the necessary margins with its brokers at the
LMEC as well as for the OTC derivative positions
with banks. The firm also reportedly faced margin
calls on its OTC trades with various banks, which it
was similarly unable to meet. The LME suspended
trading, canceled all contracts executed on the
morning of March 8, and deferred physical delivery
of maturing contracts. The LME cited orderly market
grounds as a reason for its decision. On the long side
of these trades were likely banks, commodity trading
companies, hedge funds, and other investors standing
to benefit from the price increases. Suspension of
these trades, while giving some relief to counterparties
holding short positions, wiped out profits of those on
the other side, leading to a widespread criticism from
market participants. Trading resumed on March 16
under daily price change limits, which were hit and
widened various times. To contain market volatility,
the LME also imposed daily price limits on other base
metals and on March 24 prohibited the submission of
orders outside the daily limit.
The author of this box is Torsten Ehlers.
If margins are not posted or contracts are can-
celed on derivatives markets, large banks acting
as dealers are left with open risk positions. While
dealer banks typically hold small net positions,
their gross positions are very large (about 1 million
metric tons in long and short positions), as they
act as intermediaries in the nickel and many other
derivatives markets (Figure 1.1.1, panel 2). Banks
take both positions on exchanges as well as positions
over the counter directly with clients. While dealers
tend to run a matched book between long and short
positions, if counterparties default or contracts are
canceled, this leaves banks with large open positions.
Indeed, several large dealer banks were reportedly
left with open short positions after March 8 due to
unpaid margins.
The current volatility in the commodities markets
can create serious market functioning problems.
Typically, prices on major commodity markets move
only a few percentage points on any given day. This
enables commodity producers to enter a substan-
tial amount of both short- and long-term hedging
contracts of shorter and longer maturity, as was
the case on March 4 before the rapid price increase
(Figure 1.1.1, panel 3). As the strike prices of out-
standing options contracts indicate, the price increase
on March 7 was already significantly beyond what
traders were taking into consideration and hedging
against (Figure 1.1.1, panel 4). During such extreme
events, counterparties may not have readily available
resources to fulfill their derivatives obligations. As
derivatives markets are important to distribute risks
among producers and consumers of commodities,
an impairment of derivatives markets may ultimately
spill over into the already strained availability of
commodities. More broadly, strains in derivatives
markets may create liquidity stress and concerns
about counterparty risk that may spill over to other
corners of the financial system.
Box 1.1. Extreme Volatility in Commodities: The Nickel Trading Suspension

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39International Monetary Fund | April 2022
Investment firms and banks Investment funds
Other financial institutions Commercial
3-month forward price (right scale)
Investment firms and banks Investment funds
Other financial institutions Commercial
Call options Put options
3. Open Interest in Nickel Forward Contracts by Maturity
as of March 4, 2022
(Metric tons)
4. Open Interest of “In-the-Money” Nickel Options at
Given Strike Price as of March 4, 2022
(Metric tons)
1. Net Trader Positions in the Nickel Derivatives Market
(Negative = net short position)
(Metric tons, left scale; US dollars, right scale)
2. Gross Trader Positions
(Metric tons)
Nickel producers (commercial traders) consistently run
short positions for hedging …
… while investment firms and banks hold the largest
gross positions.
Figure 1.1.1. The Nickel Market Short Squeeze in March 2022
A large amount of nickel forward contracts stuck before
the price increase is still outstanding.
All call options outstanding on March 4, 2022, were
“in-the-money” at prices prevailing on March 7/8, 2022.
Sources: Bloomberg Finance L.P.; London Metal Exchange; and IMF staff calculations.
Note: Panel 4 depicts open interest (that is, active long positions) for all call options at or above the strike price and put options at or
below the strike price (“in-the-money” options). Options have a maturity of maximum two years but mature mostly in 2022.
20,000
25,000
30,000
35,000
40,000
45,000
50,000
–400,000
–300,000
–200,000
–100,000
0
100,000
200,000
300,000
D
ec
. 2
02
1
Ja
n.
2
2
Ja
n.
2
2
Ja
n.
2
2
Ja
n.
2
2
Fe
b.
2
2
Fe
b.
2
2
Fe
b.
2
2
Fe
b.
2
2
M
ar
. 2
2
M
ar
. 2
2
0
50,000
100,000
150,000
200,000
250,000
300,000
M
ar
. 2
02
2
Ap
r.
2
2
M
ay
2
2
Ju
ne
2
2
Ju
ly
2
2
Au
g.
2
2
Se
p.
2
2
O
ct
. 2
2
N
ov
. 2
2
D
ec
. 2
2
20
23
20
24
–2
6
10
,0
00
20
,0
00
30
,0
00
40
,0
00
50
,0
00
Maturity Strike price (US dollars per metric ton)
D
ec
. 2
02
1
Ja
n.
2
2
Ja
n.
2
2
Ja
n.
2
2
Ja
n.
2
2
Fe
b.
2
2
Fe
b.
2
2
Fe
b.
2
2
Fe
b.
2
2
M
ar
. 2
2
0
50,000
100,000
150,000
200,000
Spot price on March 7
(next trading day)
–1,500,000
–1,000,000
–500,000
0
500,000
1,000,000
1,500,000
Box 1.1 (continued)

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40 International Monetary Fund | April 2022
References
Arslanalp, Serkan, and Takahiro Tsuda. 2014. “Tracking
Global Demand for Emerging Market Sovereign Debt.”
IMF Working Paper 14/39, International Monetary Fund,
Washington, DC.
Bains, Parma, and Nobu Sugimoto. Forthcoming. “Regulation of
Crypto Assets: A Closer Look at the Crypto Ecosystem.” Fin-
Tech Note, International Monetary Fund, Washington, DC.
Binance. 2022. “Ukraine, Russia, Sanctions and Crypto.”
Blog (March 4).
Blas, J. 2022. “Too-Big-to-Fail Risk Looms over Commodities.”
Bloomberg Opinion (March 16).
Brunnermeier, Markus K., and Lasse Heje Pedersen. 2009.
“Market Liquidity and Funding Liquidity.” Review of Finan-
cial Studies 22 (6): 2201–38.
Chainalysis. 2022. “Cryptocurrency Brings Millions in Aid to
Ukraine, but Could It Also Be Used for Russian Sanctions
Evasion?” Blog (March 28).
Dixit, Robert K., and Robert A. Pindyck. 1994. Investment
under Uncertainty. Princeton, NJ: Princeton University Press.
European Central Bank. 2017. “Can Commodity Trading Firms
Create Systemic Risk via Derivatives Markets?” Box 7, Finan-
cial Stability Review, Frankfurt, November.
European Securities and Market Authority (ESMA). 2020.
“Recommendation of the European Systemic Risk
Board on Liquidity Risk in Investment Funds.” Report
ESMA34-39-1119. Paris. www .esma .europa .eu/ sites/
default/ files/ library/ esma34 -39 -1119 -report _on _the _esrb
_recommendation _on _liquidity _risks _in _funds .
Financial Conduct Authority (FCA). 2022. “FCA to
Consult on Use of ‘Side Pockets’ for Retail Funds
with Exposure to Sanctioned and Suspended Russian
Assets.” March 16.
Financial Stability Board (FSB). 2020. “Enhancing Cross-border
Payments. Stage 3 Roadmap.” Basel. https:// www .fsb .org/ wp
-content/ uploads/ P131020 -1 .
Granato, Andrew, and Andy Polacek. 2019. “The Growth and
Challenges of Cyber Insurance.” Chicago Fed Letter 426.
Goel, Rohit, and Sheheryar Malik. 2021. What Is Driving the Rise
in Advanced Economy Bond Yields? IMF Global Financial Stability
Note 2021/03, International Monetary Fund, Washington, DC.
Goel, Rohit, Fabio Natalucci, and Deepali Gautam. Forthcoming.
“Evolution of the Sustainable Finance Ecosystem in Emerg-
ing Markets.”
He, Dong, Annamaria Kokenyne Ivanics, Inutu Lukonga,
Nadine Schwarz, Melo Fabiana, Herve Tourpe, and Jeanne
Verrier. Forthcoming. “Capital Flow Management Measures
in the Digital Age (1): Challenges of Crypto Assets.” FinTech
Note, International Monetary Fund, Washington, DC.
Iancu, Alina, Niel Meads, Martin Mulheisen, and Yiqun Pu.
2020. “Glaciers of Global Finance: The Currency Composition
of Central Banks’ Reserve Holdings.” IMFBlog, December 16.
Intergovernmental Panel on Climate Change (IPCC). 2022.
IPCC Sixth Assessment Report. Climate Change 2022: Impacts,
Adaptation, and Vulnerability. Geneva.
International Monetary Fund (IMF). 2022. 2021 China Article
IV Consultation. IMF Country Report No. 2022/021.
https:// www .imf .org/ en/ Publications/ CR/ Issues/ 2022/ 01/
26/ Peoples -Republic -of -China -2021 -Article -IV -Consultation
-Press -Release -Staff -Report -and -512248.
Wolff, Josephine. 2021. “How the NotPetya Attack Is Reshaping
Cyber Insurance.” Brookings Tech Stream, Brookings Institu-
tion, Washington, DC. https:// www .brookings .edu/ techstream
/how -the -notpetya -attack -is -reshaping -cyber -insurance.

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Introduction
The increase in public debt in the wake of the
COVID-19 pandemic has reinforced the relation-
ship between sovereigns and banks in emerging
market economies. The average public-debt-to-GDP
The authors of this chapter are Andrea Deghi (team lead), Salih
Fendoglu, Tara Iyer, Oksana Khadarina, Hamid Reza Tabarraei, Yizhi
Xu, Dmitry Yakovlev, and Mustafa Yasin Yenice, under the guidance
of Fabio Natalucci, Mahvash Qureshi, and Jérôme Vandenbussche.
Viral Acharya served as an expert advisor.
ratio in emerging markets surged to a record 67 per-
cent in 2021 from about 52 percent before the
pandemic, as economic activity declined and govern-
ments greatly increased fiscal support to nonfinan-
cial firms and households to cushion the impact of
the crisis (Figure 2.1, panel 1).1 Although public
1Henceforth, the chapter uses the shorthand “firms” for nonfinan-
cial firms; that is, small, medium, and large enterprises other than
banks and other financial institutions.
Chapter 2 at a Glance
• Holdings by banks of domestic sovereign debt have surged in emerging markets during the COVID-19
pandemic, on average accounting for about one-fifth of banking sector assets and 200 percent of their
regulatory capital.
• The larger holdings of domestic sovereign debt by emerging market banks have deepened the ties between
the sovereign and banking sectors—the so-called sovereign-bank nexus. With public debt at historically
high levels and the sovereign credit outlook deteriorating in many emerging markets, a deeper nexus poses
risks of an adverse feedback loop that could threaten macro-financial stability.
• This chapter examines the sovereign-bank nexus in emerging markets, focusing especially on the COVID-19
pandemic, and puts forward policy options to minimize its potential risks and enhance resilience.
• The transmission of risks between the sovereign and banking sectors is significant—both directly and
indirectly through the nonfinancial corporate sector.
• An increase in sovereign risk can adversely affect banks’ balance sheets and lending appetite, especially in
countries with less-well-capitalized banking systems and higher fiscal vulnerabilities. It can also constrain
funding for the nonfinancial corporate sector and reduce its capital expenditure.
• Amid tightening global financial conditions, heightened geopolitical tensions, and large public financing needs,
emerging markets face complex policy trade-offs. Given the multifaceted nature of the sovereign-bank nexus,
the policy response to mitigate risks must be tailored to country-specific circumstances and should include:
o Better targeting of spending and strengthening of medium-term fiscal frameworks in countries with limited
fiscal space and tight borrowing constraints to build resilience and mitigate the impact of an adverse shock
o Preserving bank resources to absorb losses by restricting capital distribution where needed
o Conducting bank stress tests by taking into account the multiple channels of the nexus
o Examining options to weaken the nexus—such as capital surcharges on banks’ holdings of sovereign
bonds above certain thresholds—once the economic recovery has taken hold and pandemic-related
financial sector support measures have been withdrawn
o Continuing efforts to foster a deep and diversified investor base to strengthen market resilience in coun-
tries with underdeveloped local currency bond markets
• Given that risks from the sovereign-bank nexus are not limited to emerging markets but have also man-
ifested in advanced economies in the past, the Basel Committee on Banking Supervision could consider
resuming its efforts to develop international standards that reflect a more risk-sensitive regulatory and
supervisory treatment. To begin with, and in order to foster market discipline, banks should be mandated
to disclose data on all material sovereign exposures.
THE SOVEREIGN-BANK NEXUS IN EMERGING MARKETS:
A RISKY EMBRACE2CHAPTE
R
International Monetary Fund | April 2022 41

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
42 International Monetary Fund | April 2022
debt levels have also risen in advanced economies,
the domestic sovereign debt exposure of banks
has increased relatively more in emerging markets
(Figure 2.1, panel 2)—reaching 17 percent of total
banking sector assets in 2021—as the additional
government financing needs have been met mostly
by domestic banks amid declining foreign participa-
tion in local currency bond markets and a generally
limited domestic investor base (Figure 2.1, panel 3).
Consequently, the linkages between the financial
health of the sovereign and banking sectors—the
so-called sovereign-bank nexus—have intensified in
these economies.
The relationship between sovereigns and banks
has also become more complex during the pandemic
as interdependencies with the real sector have deep-
ened. Countries across the world have supported the
liquidity and solvency of firms through unprecedented
policy measures, including accommodative monetary
policy and fiscal measures such as cash transfers, equity
injections, loans, and guarantee programs. In emerg-
ing markets, the discretionary fiscal response to the
pandemic averaged about 10 percent of GDP during
2020–21—of which 6 percent consisted of additional
spending and forgone revenues and 4 percent consisted
of equity, loans, and guarantees. In turn, the corporate
sector has become highly dependent on the continu-
ation of policy support in cases where the economic
recovery has yet to firmly take hold and corporate
vulnerabilities are high (Figure 2.1, panel 4). This has
AEs EMs
Public debt in US dollars (right scale)
AEs EMs
Banks
Foreign
Other domestic
Central banks
Figure 2.1. Developments in Emerging Market Public Debt and Banks’ Sovereign Exposures
Public debt has surged in emerging markets …
1. Public Debt, 2005–21
Pe
rc
en
t o
f G
D
P
0
150
30
60
90
120
Tr
ill
io
ns
o
f c
ha
in
ed
2
01
0
US
d
ol
la
rs
0
80
20
40
60
2020 20212005–09 2010–14 2015–19
… and banks’ domestic sovereign debt exposure has reached historic
highs …
2. Banks’ Domestic Sovereign Debt Exposure, 2005–21
(Percent)
Pe
rc
en
t o
f b
an
ki
ng
s
ec
to
r
as
se
ts
0
5
10
15
20
2020 20212005–09 2010–14 2015–19
… as banks have been the main buyers of domestic debt.
3. Change in Local Currency Sovereign Bond Holdings
(Billions of US dollars, cumulative change since end-2019)
Fiscal policy has supported firms during the pandemic.
–100
900
100
300
500
700
Apr.
20
July
20
Oct.
20
Jan.
21
Apr.
21
July
21
Oct.
21
Jan.
2020
4. Corporate Debt and Fiscal Support during the COVID-19 Crisis, 2021
(Percent of GDP)
Fi
sc
al
s
up
po
rt
0
10
5
15
20
25
75 100 1250 25 50
Nonfinancial corporate debt
Sources: Fitch Connect; IMF, Monetary and Financial Statistics, World Economic Outlook, and Fiscal Monitor databases; and IMF staff calculations.
Note: In panels 1 and 2, indicators are country averages weighted by purchasing-power-parity GDP. Public debt is in real terms; that is, in trillions of chained 2010
US dollars. In panel 2, banks’ sovereign exposure corresponds to claims on central government debt divided by total banking sector assets. Advanced economies
comprise economies classified as advanced in the IMF World Economic Outlook database. In panel 4, fiscal support corresponds to the discretionary fiscal support
announced or taken during the COVID-19 crisis expressed as a percent of GDP. For 2021, fiscal support and the corporate-debt-to-GDP ratio shown in the panel
correspond to September data. See Online Annex 2.1 for countries in the emerging market sample. AEs = advanced economies; EMs = emerging markets.

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43International Monetary Fund | April 2022
significantly deepened the interconnectedness of sov-
ereigns and banks through firms, so that stress in the
sovereign sector could spill over quickly to firms and
hurt banks’ balance sheets.2
Emerging markets are particularly vulnerable to the
macro-financial stability risks associated with a strong
sovereign-bank nexus in the face of an adverse shock
as global financial conditions tighten. Growth pros-
pects are generally weaker relative to the pre-pandemic
trend in emerging markets compared with advanced
economies (see the April 2022 World Economic
Outlook), while governments’ ability to support the
economic recovery through increased spending or
reduced revenues (fiscal space) is more limited, with
a higher debt-servicing burden (Figure 2.2, panel 1).
The public-debt-to-GDP ratio is thus projected to
continue to grow in several emerging markets over
the medium term, while it is expected to decline in
advanced economies (Figure 2.2, panel 2). At the same
time, refinancing risks are higher in emerging markets
given the shorter average maturity profile of public
debt compared with advanced economies (see the
October 2021 Fiscal Monitor), a higher share of public
debt denominated in foreign currency (especially in US
dollars), and rising sovereign spreads amid a worsening
sovereign credit outlook (Figure 2.2, panels 3–5). Local
currency government bond yields have also increased
for most emerging markets in recent months as foreign
participation in local currency bond markets has
declined, while central banks have tightened mone-
tary policy on the heels of rising inflationary pressures
(Figure 2.2, panel 6; see also Chapter 1).
Amid higher fiscal vulnerabilities, a sharp tightening
in global financial conditions on the back of mone-
tary policy normalization in advanced economies and
intensifying geopolitical tensions caused by the conflict
between Russia and Ukraine could push emerging
market borrowing costs higher and potentially trigger an
adverse feedback loop between the sovereign and bank-
ing sectors through multiple channels.3 For example,
2The sovereign-bank nexus has strengthened in some advanced
economies as well, particularly in Europe. ECB (2020) documents
considerable heterogeneity in banks’ sovereign debt exposure across
European countries and notes that banks’ vulnerability to higher
holdings of sovereign debt securities has been contained during the
pandemic, since valuation changes have been modest.
3Commodity-importing emerging markets may be particularly
at risk as they face the prospect of tighter global financial
conditions and high commodity prices putting pressure on their
external accounts.
with public debt already elevated, higher sovereign bor-
rowing rates could fuel debt sustainability concerns and
adversely affect banks’ funding conditions and balance
sheets through their exposure to sovereign debt.4 In this
regard, it is worth noting that countries whose banks
are more exposed to sovereign debt are also those with a
higher public-debt-to-GDP ratio and lower bank capital
ratios (Figure 2.3, panels 1 and 2; see also Chapter 1).
Sovereign stress could thus potentially quickly trans-
mit to the banking sector in these economies.5 Tighter
borrowing constraints could also reduce governments’
ability to support banks through implicit or explicit
guarantees (the safety net), increasing stress in the bank-
ing sector and, in turn, raising the need for actual fiscal
support and further weakening the sovereign balance
sheet. In addition, a widening of sovereign spreads amid
constrained fiscal space could lead to a rapid withdrawal
of policy support to the real economy, hurting economic
growth and intensifying bank losses that could further
magnify the sovereign stress.
Domestic shocks such as a weaker-than-anticipated
economic recovery in emerging markets amid the
spread of new COVID-19 variants could also unleash
the pernicious dynamics of the sovereign-bank nexus.
For example, a decline in economic activity could
put public finances under pressure and worsen the
sovereign credit outlook, leading to an increase in
sovereign funding costs. A substantial rise in corporate
bankruptcies could also undermine banks’ capital ade-
quacy and diminish their willingness to lend, further
undermining economic activity and straining sovereign
balance sheets.6
Against this backdrop, this chapter examines the
relevance of the sovereign-bank nexus in emerg-
ing markets for macro-financial stability and puts
forward policy options to minimize potential risks
and enhance resilience. Building on earlier research
on the topic, which has focused mostly on advanced
4These effects could be aggravated if tighter global financial
conditions were accompanied by a large reversal in capital flows from
emerging markets, inducing sharp currency depreciation and raising
the domestic currency burden of liabilities denominated in foreign
currency (Chapter 1 of the April 2022 Fiscal Monitor).
5In some major emerging markets, banks hold floating-rate bonds,
inflation-indexed bonds, and “non-defaultable” bills issued by central
banks, which may be less sensitive to interest rates and sovereign risk
and could provide some insulation from a rise in sovereign risk.
6Although banks remain generally well capitalized in emerging
markets, pandemic-related regulatory flexibility and other supportive
financial sector policy measures make it difficult to precisely ascertain
the true health of the banking system at this time.

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
44 International Monetary Fund | April 2022
9,070
3,960
8,280
1,360
AEs EMs EMs AEs (right scale)
Net rating downgrade
Net outlook downgrade
Asia Europe Latin America
Middle East and Africa
Figure 2.2. Fiscal Vulnerabilities in Emerging Markets
Emerging markets have much higher debt-service burdens relative to
advanced economies …
1. Interest Payments to Revenue, 2013–26
(Percent)
Projected
2013 15 17 19 21 23 25
3
6
9
12
15
2. Projected Public-Debt-to-GDP Ratio, 2022–26
(Percent of GDP)
… and rising debt levels in the medium term.
2022 23 24 25 26
110
112
114
116
65
70
75
3. Share of Foreign Currency Debt in Total Public Debt, 2021
(Percent)
A large share of sovereign debt is denominated in foreign currency.
AEs EMs
0
5
10
15
20
4. Net Emerging Market Sovereign Rating Downgrades and
Net Negative Outlook
(Number of sovereigns, 12-month sum)
The sovereign credit outlook has worsened in emerging markets …
Jan.
2019
July
19
Jan.
20
July
20
Jan.
21
July
21
Jan.
22
–6
–10
–2
2
6
10
14
18
… and spreads are above pre-pandemic levels.
5. Change in Emerging Market Sovereign Credit Spread by Sovereign
Rating, 2020–22
(Basis points)
–100
300
700
1,100
AAA AA A+ A– BBB BB+ BB– B CCC+ CCC–
6. Average Yields of JPMorgan Global Bond Index by Region 2013–22
(Percent)
Local currency government bond yields have also risen for most
emerging markets in recent months.
2013 14 15 16 17 18 19 20 21 22
2
4
6
8
10
12
14
Sources: Bloomberg Finance L.P.; Fitch Connect; JPMorgan EMBI Global; Standard & Poor’s Capital IQ; and IMF staff calculations.
Note: In panels 1–3, indicators are country averages weighted by purchasing-power-parity GDP. In panel 4, changes in sovereign rating and rating outlook are
computed using a 12-month rolling sum based on changes reported by Standard & Poor’s. Panel 5 shows the difference in credit spreads between December 31,
2020, and March 11, 2022. Spreads are calculated as the difference between a bond’s yield and the linearly interpolated yield of the two base curve bonds that
bracket the maturity of this bond. In panel 6, the drop in average yields for Europe in the second week of March 2022 reflects the exclusion of Russia from the
JPMorgan index. AEs = advanced economies; EMs = emerging markets.

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45International Monetary Fund | April 2022
economies,7 the chapter explores the strength of the
nexus in emerging markets, especially during periods
of sovereign stress, and the key channels of trans-
mission.8 Specifically, relying on a comprehensive
conceptual framework and drawing on data from
the past two decades for a broad sample of emerging
7The linkage between sovereign and banking sector risk has
been well explored for advanced economies, especially in the
context of the euro area sovereign debt crisis (for example, Acharya
and others 2018; Dell’Ariccia and others 2018). The findings
of these studies, however, may not be generalizable to emerging
markets, which have different structural characteristics—notably
in terms of lower financial sector development, a greater share of
foreign-currency-denominated public debt, and higher sensitivity to
external shocks. Gennaioli, Martin, and Rossi (2018) and Feyen and
Zuccardi Huertas (2019) document the existence of a sovereign-bank
nexus in emerging markets using pre–COVID-19 pandemic data.
IMF (2022) discusses the deepening of the sovereign-bank nexus in
recent years in the context of South Africa.
8Although shocks to the banking sector could also trigger the
feedback loop, the elevated fiscal vulnerabilities in emerging markets,
combined with the risk of a sharp tightening in global financial con-
ditions as monetary policy normalizes in advanced economies, makes
an increase in sovereign stress more relevant at the current juncture.
markets,9 the chapter investigates the following
key questions:
• How has the link between the sovereign and bank-
ing sector evolved, and how has the COVID-19
pandemic affected that link? What factors motivate
the banking sector to hold sovereign debt?
• How strong is the sovereign-bank nexus? How is it
affected by adverse shocks such as a tightening in
global financial conditions?
• How relevant are the various channels of transmis-
sion? To what extent does sovereign stress transmit
directly to banks through their exposure to gov-
ernment bonds? How much do banks benefit from
government guarantees, especially during episodes of
sovereign stress? And to what degree does sovereign
stress affect the real economy—in particular the
corporate sector, which may in turn affect banks?
9The core sample of emerging markets comprises 53 economies.
The specific sample of economies across empirical exercises and the
time period covered depend on data availability. See Online
Annex 2.1 for details. All online annexes are available at www .imf
.org/ en/ Publications/ GFSR.
TUR
ZAF
ARGBRA
COL
CRI
SLV
GTM
MEX
PANPER URY
IND
IDN
MYS
PAK
PHL
THA
VNM
GHA
MUS
ARM
BLR
ALB
GEO
KAZ BGR
RUS
CHN
UKR
HRV
MKD
BIH
POL
ROU
TUR
ZAF
ARG
BRA
CHL COL
CRI
DOM
ECU
SLV
GTM
MEX
PANPER
URY
JAM
JOR
EGY
IND
IDN
MYS
PAK
PHL
THA
VNM
DZA
AGO
GHA
MAR
TUN
ARM
AZE
BLR
ALB
GEO
KAZ
BGR
RUS
CHN
UKR
SRB
HUN
HRV
MKDBIH
POL
ROU
1. Sovereign Debt and Banks’ Holdings of Public Debt, 2021
(Percent)
2. Tier 1 Capital-to-Total-Assets Ratio and Banks’ Holdings of
Sovereign Debt, 2021
(Percent)
Banks’ exposure to sovereign debt is greater in economies with higher
public debt …
… and lower bank capital.
Sources: Fitch Connect; IMF, Financial Soundness Indicators, Monetary and Financial Statistics, and World Economic Outlook databases; and IMF staff calculations.
Note: In panel 1, red dots reflect provisional public-debt-to-GDP ratios in 2021 vis-à-vis banks’ central government debt holdings in 2021 (third quarter). In panel 2,
total assets are used in the denominator of the Tier 1 capital ratio (instead of risk-weighted assets) to provide greater comparability across countries. Given limited
country-level data availability, banks’ sovereign debt exposures for India and Argentina are computed using bank-level Fitch Connect data. Data labels use
International Organization for Standardization (ISO) country codes.
Figure 2.3. Banks’ Exposure to Sovereign Debt in Emerging Markets
0
10
20
30
40
50
0
10
20
30
40
50
15 55 7535 95 5 8 11 14
Ratio of public debt to GDP Ratio of Tier 1 capital to total assets
Ra
tio
o
f b
an
ks
’ s
ov
er
ei
gn
d
eb
t h
ol
di
ng
s
to
to
ta
l a
ss
et
s
Ra
tio
o
f b
an
ks
’ s
ov
er
ei
gn
d
eb
t h
ol
di
ng
s
to
to
ta
l a
ss
et
s

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
46 International Monetary Fund | April 2022
Sovereign-Bank Interlinkages:
Conceptual Framework
The sovereign and banking sectors are connected
through three key channels that facilitate the transmis-
sion of shocks from one sector to the other, interacting
with and magnifying vulnerabilities in each sector and
generating adverse feedback loops (Figure 2.4). The first
channel stems from the direct exposure of banks to sover-
eign risk through their holdings of government debt. A
rise in sovereign spreads could reduce the market value
of government debt that banks hold and use as collateral
to secure financing. As a result, banks could face higher
funding costs and liquidity strains, potentially restricting
their capacity to lend to the real economy.10
10A haircut applied to government debt exposures will lead
to capital losses for banks unless the losses have already been
absorbed by provisioning and mark-to-market accounting. As noted
in IMF (2021), a timely and carefully designed domestic debt
restructuring can limit the losses for banks and the impact on the
broader economy.
The second channel relates to the safety net, or
government support provided to banks in the form
of implicit and explicit guarantees.11 Sovereign stress
could reduce these funding benefits, threatening the
stability of banks. A weaker banking sector may in
turn increase the need to activate the guarantees,
straining fiscal accounts and further aggravating
pressures on the sovereign. In some emerging markets,
governments hold substantial bank equity, which could
lead to additional fiscal losses (on top of potential
recapitalization needs) if banks face financial pressure.
11Such guarantees are provided to support banks and reduce the
likelihood of a financial disruption if the banking sector comes
under severe financial stress. As discussed later in the chapter, this
channel is likely to be stronger for domestic state-owned banks—
which are also more likely to be financing the fiscal deficit, relaxing
the government’s borrowing constraint and potentially leading to
greater public debt accumulation. Because these banks also tend to
be subject to limited market discipline and weak governance and
supervision, they could pose additional financial stability risks (Feyen
and Zuccardi Huertas 2019).
Source: IMF staff.
Note: A sudden tightening of global financial conditions is one type of shock that may trigger an adverse sovereign-bank feedback loop. Other possible shocks
include a terms-of-trade shock that may affect the sovereign, banking, and corporate sectors; a domestic banking crisis triggered by a deposit run that could disrupt
credit supply to the corporate and household sectors, reducing economic activity and leading to fiscal sustainability pressures; and a shock to economic activity, for
example, because of a health crisis or natural disaster, which could strain sovereign and banking sector balance sheets.
Figure 2.4. Key Channels of the Sovereign-Bank Adverse Feedback Loop
Mark-to-market loss on sovereign bond holdings and
higher funding costs for banks
Lower demand for sovereign bonds and higher
funding costs for sovereign
Weaker backstops and higher funding costs for banks
Higher contingent liabilities
(resolution policies)
Ti
gh
te
ni
ng
o
f g
lo
ba
l
fin
an
ci
al
c
on
di
tio
ns
Fo
re
ig
n
in
ve
st
or
s
In
cr
ea
si
ng
c
re
di
t r
is
k
In
cr
ea
si
ng
c
ur
re
nc
y
an
d
fu
nd
in
g
in
de
x
Macroeconomic
channel
– Lower
spending and
transfers/
economic
slowdown
– Downward
pressure on
corporate
ratings
– Tighter
lending and
funding
conditions
– Crowding out
– Lower tax revenues
– Higher contingent
liabilities
Higher nonperforming
loans and funding
costs
Corporate sector
Sovereign Banks
Macroeconomic
channel
Safety net channel
Sovereign exposure channel

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47International Monetary Fund | April 2022
The third channel refers to the indirect feedback
loop effect between sovereigns and banks through the
broader macroeconomy, in particular the corporate sec-
tor. A weakening of the sovereign balance sheet could
hurt the corporate sector by raising borrowing costs,
or through fiscal consolidation (for example, by raising
taxes or reducing expenditure) and policy uncertainty.
It may also increase the burden on domestic banks to
finance government debt, crowding out bank lend-
ing to the corporate sector and affecting economic
activity.12 A weaker corporate sector could in turn have
a negative impact on banks’ balance sheets because of
possible deterioration of its loan portfolio quality and
higher credit provisioning. Subsequently, stress in the
banking sector could disrupt economic activity even
further, impairing government finances and transmit-
ting stress back to the sovereign.
These three channels could also work in reverse—
that is, stress in the banking sector could lead to
sovereign stress—for example, by disrupting the gov-
ernment bond market, activating fiscal backstops, or
dampening economic activity. Moreover, these three
channels tend to feed into one another as financial
conditions tighten, thus transmitting and amplify-
ing shocks from one sector to the other, weakening
balance sheets and creating a mutually reinforcing
vicious “doom loop.”13
That said, well-capitalized banks could also serve
as a shock absorber in times of distress by acting as a
stable buyer of sovereign debt, especially in countries
with a limited domestic investor base. Nevertheless,
the overreliance of governments on the domestic
banking sector for their financing needs is a source of
significant risk—for example, by leading to a more
12“Crowding out” refers to less bank credit to the private sector
because of increased lending to the government. Sovereign distress
may crowd out bank lending as banks may be forced to hold more
sovereign debt (moral suasion) when sovereign refinancing needs
are typically higher. Banks may also engage in risk shifting and may
choose to hold more government debt to profit from higher yields.
For emerging markets, there is evidence of lower private sector credit
growth during times of sovereign stress.
13The extent of the feedback loop may be affected by monetary
policy. In an adverse scenario, a loosening of monetary policy
(including large asset purchases) could reduce the severity of the loop
by supporting economic growth and lowering domestic borrowing
costs for sovereigns, banks, and firms. Furthermore, in emerging
markets, the strength of the sovereign-bank nexus may also be
affected by a “currency channel,” by which an external shock that
triggers a currency depreciation could deepen sovereign and banking
stress through balance sheet effects.
concentrated investor base and greater potential to
amplify shocks.14
Another possible source of interconnection between
sovereigns, banks, and firms is the role played by
domestic nonbank financial institutions in many
emerging markets. A rise in sovereign (or banking) sec-
tor risk may transmit to these institutions, which could
further amplify vulnerabilities in each sector through
direct and indirect exposures (both to banks and firms)
and magnify the impact of the shock. Nonbank finan-
cial institutions hold a nontrivial share of public debt
in some emerging markets (see Box 2.2.1 in Online
Annex 2.2), but potential distress caused by these
institutions may be more limited, as financial systems
remain largely bank-based in emerging markets.15
Relevance of the Sovereign-Bank Nexus in
Emerging Markets: Some Stylized Facts
Domestic banks have traditionally been important
players in sovereign bond markets in emerging markets
both as investors and market makers. Their share in
sovereign debt holdings increased gradually from an
average of about 20 percent two decades ago to more
than 30 percent in 2020 (Figure 2.5, panel 1), but it
varies considerably across countries. In some economies
(such as Uruguay), banks hold less than 10 percent of
total sovereign debt, while in others (such as China)
this share exceeds 80 percent.16 In addition to banking
sector solvency and liquidity regulations, which incen-
tivize the holding of domestic sovereign debt relative to
other claims (BCBS 2017, 2021), several other factors
explain banks’ exposure to sovereign debt, including
14Financial stability risks are also associated with the holding
of government debt by nonbank financial institutions and foreign
investors. For example, mutual funds could be prone to selling
government securities in times of stress to meet liquidity needs,
contributing to pressures in government bond markets. Foreign
investors also tend to be skittish, and their quick withdrawal from
government bond markets can create liquidity problems. Thus, the
investor base needs to be well diversified to avoid overreliance on any
one type of investor.
15Lack of detailed data on sovereign debt holdings of different
types of nonbanking financial institutions in emerging markets
(investment funds, insurance companies, pension funds, and so on),
as well as on their interconnectedness with other sectors, precludes
an in-depth analysis of their role in the sovereign-bank nexus in
this chapter.
16In some emerging markets, banks’ sovereign debt exposure
declined over the past decade, as nonresident investor participation
in local currency bond markets rose. This trend, however, reversed
during the pandemic (Online Annex Figure 2.3.1).

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
48 International Monetary Fund | April 2022
liquidity management, higher interest rates, lower
financial sector development, and government moral
suasion (Box 2.1).17
The overreliance of governments on domestic banks
for their financing needs, and the associated high
17The use of domestic government bonds for liquidity manage-
ment (such as to access central bank liquidity) can be a key driver
of banks’ preference to hold domestic rather than foreign bonds,
resulting in a significant home bias. Asonuma, Bakhache, and Hesse
(2015) show that when banks exhibit higher home bias, fiscal con-
solidation by the sovereign tends to be slower, all else equal.
exposure of banks to sovereign debt, increases the like-
lihood of shock transmission between the two sectors.
The default risks of sovereigns and banks—proxied
by the expected default frequency—tend to move in
lockstep in emerging markets (Figure 2.5, panel 2).
Importantly, the strength of this relationship varies
with the level of distress in the banking sector: at low
levels of bank distress, a 1 percentage point increase
in sovereign default risk is associated with a 0.4 basis
point increase in banks’ expected default frequency
(Figure 2.5, panel 3). However, at higher levels of
Sovereign Banks
Estimate 90% confidence interval Sovereign–banks Sovereign–NFCs
Banks–NFCs Global financial conditions
(right scale)
Figure 2.5. Association between Emerging Market Sovereign and Banking Sector Default Risk
Domestic banks are major players in the sovereign debt market.
1. Share of Domestic Banks’ Holding in Total Government Debt,
2005–20
(Percent)
0
20
40
60
80
100
2005 07 09 11 13 15 17 19
Sovereign and bank default risk move together …
2. Sovereign and Bank Expected Default Frequencies, 2006–21
(Percent, average across countries)
0
1
2
3
4
2006 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21
… and the correlation increases at higher levels of bank stress …
3. Relationship between Changes in Sovereign and Bank Expected
Default Frequencies, 2006–21
(Basis point)
–10
–5
0
5
10
15
Higher bank distress
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
Percentile
… as well as when global financial conditions are strained.
4. Median Correlation among Sovereign, Bank, and Nonfinancial
Corporate Sector Stress and Global Financial Conditions,
2008:M1–2021:M9
(Index)
–6
–3
0
3
6
9
0.0
0.2
0.4
0.6
0.8
2008 10 12 14 16 18 20 22
Sources: Arslanalp and Tsuda (2014); Moody’s; Refinitiv Datastream; and IMF staff calculations.
Note: Panel 1 shows the unweighted average of the domestic banks’ share in general government debt. Bands refer to the minimum and maximum value of this
share in the sample. In panel 2, banking sector expected default frequency (EDF) is equal to the average EDF of individual banks. Panel 3 shows the strength of the
correlation between changes in banks and sovereign default risk at different values of bank stress calculated using a panel quantile regression with country fixed
effects. Default risk is measured by the EDF. Higher bank distress refers to periods with larger changes in the banking sector EDF. Dots correspond to the effect of a
change in sovereign EDF by 1 percentage point on the change in banks’ EDF as computed by panel quantile regressions with country fixed effects. Panel 4 shows the
median time-varying correlation between changes in sovereign, bank, and nonfinancial corporation EDFs across countries using a 24-month rolling window. The
median correlation is a number between –1 and 1. The global financial conditions indicator refers to the common component of monthly equity price returns
estimated across advanced economies and emerging markets using a factor-augmented vector autoregressive model. NFCs = nonfinancial corporations.

C H A P T E R 2 T h E S O v E R E I G N – B A N k N E x U S I N E M E R G I N G M A R k E T S : A R I S k Y E M B R A C E
49International Monetary Fund | April 2022
distress, the association is 10 times stronger. The
relationship is also much tighter when global financial
conditions are under strain, as is evident from the
jump in the correlation between sovereign and bank
default risk during the global financial crisis and at
the onset of the COVID-19–related financial market
turmoil in March 2020 (Figure 2.5, panel 4).18
The strong association between sovereign and
banking sector risks has amplified past financial crises.
Banking and sovereign debt crises have been particu-
larly prevalent in emerging markets, frequently occur-
ring at the same time or in succession (Figure 2.6,
panel 1). Their incidence typically increases in con-
junction with a tightening in global financial condi-
tions. This tends to induce a reversal in cross-border
18Similar dynamics are observed for the correlation of sovereign
and banking sector stress with nonfinancial corporate sector stress,
which provides further evidence of the strengthening of relationships
among the three sectors when global financial conditions tighten.
capital flows, making it more difficult for both sover-
eigns and banks to obtain funding, while also leading
to sharp currency depreciations (or a currency crisis)
that further strain sovereign and bank balance sheets
(Reinhart and Rogoff 2009).
These mechanisms were at work in several prom-
inent emerging market sovereign debt and financial
crises of the late 1990s and early 2000s (for example,
Argentina, Ecuador, Russia). In some cases, govern-
ments increasingly relied on domestic banks to fund
deteriorating fiscal positions, making a banking crisis
unavoidable after the eventual sovereign default.19 The
fiscal cost of restructuring and supporting the financial
sector associated with banking crises, however, has also
been significant in emerging markets (and on par with
19On average, government bond holdings of banks in emerging
markets increase by about 7 percentage points after a sovereign debt
crisis, while they tend to decline in advanced economies (see Online
Annex Figure 2.3.2).
EMs AEs
1. Frequency of Sovereign Default, Banking, and Currency Crises in
Emerging Markets and Advanced Economies, 1971–2016
(Percent)
2. Financial and Fiscal Costs of Banking Crises, 1971–2016
(Percent)
Banking and sovereign debt crises have often occurred together in
emerging markets.
Fiscal costs of banking crises have been significant in emerging
markets.
Figure 2.6. Sovereign Debt and Banking Crises in a Historical Context: Emerging Markets versus Advanced Economies
0
5
10
15
20
25
30
Peak NPLs Fiscal cost Liquidity support
Sources: Emerging Portfolio Fund Research; Harvard Business School, Global Crises Data by Country; Laeven and Valencia (2018); IMF, Monetary and Financial
Statistics database; and IMF staff calculations.
Note: In panel 1, banking, sovereign, and currency crises are those identified by Global Crises Data by Country, updated by the Harvard Business School based on
Reinhart and Rogoff (2009). Currency crises are defined as an annual depreciation in the nominal exchange rate of at least 15 percent. The frequency of occurrence
of each type of crisis is computed as the total number of country-year observations identified as the corresponding crisis as a percent of the total number of
country-year observations in the sample. In panel 2, information is sourced from Laeven and Valencia (2018). Fiscal costs refer to outlays directly related to financial
sector restructuring as percent of GDP. Nonperforming loans are expressed in percent of total loans. Liquidity support is measured as the ratio of central bank claims
on deposit money banks and liquidity support from the Treasury to total deposits and liabilities to nonresidents. AEs = advanced economies; EMs = emerging
markets; NPLs = nonperforming loans.
Sovereign (domestic)
Sovereign (external)
Banking
Currency
Banking and sovereign
Type of crisis EMs AEs
6.3 0.1
18.5 0.5
15.0 16.1
25.8 10.9
6.6 0.5
0.0Banking, sovereign, and currency 5.1

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50 International Monetary Fund | April 2022
advanced economies), suggesting a possible transmis-
sion of banking stress back to the sovereign. Further-
more, the deterioration in credit quality (proxied by
a high share of nonperforming loans in total loans)
during banking crises has been twice as large in emerg-
ing markets as in advanced economies, indicating the
existence of a strong macroeconomic channel in the
former group (Figure 2.6, panel 2).
Deepening of the Sovereign-Bank Nexus during
the COVID-19 Pandemic
The relationship between sovereigns and banks
in emerging markets has been reinforced during the
COVID-19 pandemic, as banks’ holdings of local
currency government debt have increased significantly
as a share of their assets (Figure 2.1, panel 2; Box 2.1).
While this increase has been driven by state-owned
banks in several countries, private domestic banks have
also played a role (Figure 2.7, panel 1). Banks’ excess
liquidity, driven by weaker credit demand and a surge
in deposits, appears to have been one factor behind
banks’ decisions to purchase more sovereign debt
(Figure 2.7, panel 2).
Banks in emerging markets are generally well capi-
talized because of reforms enacted following the global
financial crisis and policy support provided during the
pandemic.20 However, sovereign debt exposure consti-
tutes a significant share of regulatory capital in some
countries (Figure 2.7, panel 3). Importantly, a sizable
share of banks’ outstanding sovereign debt holdings
follows mark-to-market accounting in several emerging
markets (Figure 2.7, panel 4), which could potentially
undermine banks’ capital adequacy if the market value
of these assets were to decline.
This risk is particularly relevant in the current
environment of monetary policy normalization in
advanced economies and rising global yields.21 To assess
its implications, a simple bank-level scenario analysis is
undertaken for individual emerging markets. The mini-
mum haircuts on banks’ holdings of domestic sovereign
debt that would lead to a breach of the 4.5 percent
20The median capital adequacy ratio across emerging markets
stood at 14 percent in 2020 (see Online Annex Figure 2.3.3), but
recent global bank stress tests point to relatively lower resilience in
emerging markets than in advanced economies.
21Higher policy rates and higher term premia will raise yields
across the term structure of interest rates, reducing the market value
of bond holdings (and capital) in bank balance sheets, even if fiscal
conditions are sound.
minimum regulatory common equity Tier 1 (CET1)
capital ratio are computed (Figure 2.7, panel 5). When
taking the median value of these haircuts across banks
in a region, the results show that banking systems in
sub-Saharan Africa are relatively more vulnerable to
sovereign distress. Haircuts as small as 30 percent, which
are probable and have already been observed in the
past, would breach the minimum CET1 capital ratio in
domestic banks in the region.22
Furthermore, banking sector health depends on the
viability of banks’ corporate borrowers, which have
faced strains during the pandemic. In most emerg-
ing markets, the sustainability of corporate debt—as
measured by earning capacity relative to debt—has
declined as corporate revenues have fallen (Online
Annex Figure 2.3.4). While it is difficult to fully
ascertain the soundness of bank balance sheets at the
current juncture because of regulatory flexibility and
other financial sector support measures in place,23
nonperforming loans are more than one-tenth of total
loans in some countries (Online Annex Figure 2.3.4)
and could edge up as loan-repayment moratoria and
other support measures are unwound (Chapter 1). An
adverse shock to firms due to a rise in sovereign risk
could thus have a significant impact on banking stabil-
ity through the macroeconomic channel.
In this economic landscape, sovereign and bank
credit risk remain closely tied in emerging markets, as
reflected by the positive correlation between sovereign
and bank credit ratings (Figure 2.7, panel 6), indi-
cating that the nexus is highly pertinent. The analysis
that follows more formally evaluates the strength of
the nexus in emerging markets and some of the key
channels of transmission.
Measuring the Strength of the
Sovereign-Bank Nexus
To assess the overall strength of the nexus in
emerging markets, two-way relationships between
the sovereign, banking, and corporate sector default
22For further context, direct loss-given-default rates for sover-
eign debt holders have varied widely, but Cruces and Trebesch
(2013) estimate a 37 percent average haircut for countries during
1978–2010 and a 50 percent average haircut during 1998–2010.
23Regulatory flexibility refers to the temporary measures adopted by
financial regulators and supervisors during the COVID-19 pandemic to
ensure that banks continued to lend to the real economy—for example,
the release of countercyclical capital buffers to free up lending capacity,
restrictions on capital distributions, and debt payment moratoria.

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51International Monetary Fund | April 2022
State-owned Private domestic Foreign subsidiary
2020 2019 2020 2019
Scenario haircut Historical haircut
Latin
America
Europe AfricaMiddle East
and Central
Asia
Asia and the
Pacific
Figure 2.7. Sovereign-Bank Nexus in Emerging Markets during the COVID-19 Pandemic
State-owned banks in several countries have been the major buyers of
government debt.
1. Share of Net Sovereign Bond Acquisitions by Type of Bank, 2020
(Percent)
–30
130
10
50
90
2. Change in the Ratio of Local Currency Sovereign Bonds to CET1
Capital versus Change in the Ratio of Loans to Deposits, 2019–20
(Percent)
Excess liquidity is associated with banks’ increased sovereign bond
holdings.
0.0
2.0
0.5
1.0
1.5
3. Ratio of Local Currency Sovereign Bond Holdings to CET1 Capital
(Ratio)
Banks’ sovereign exposure relative to capital has increased during the
pandemic.
0.0
3.0
0.5
1.0
1.5
2.0
2.5
4. Share of Mark-to-Market Sovereign Bonds
(Percent of total)
A sizable share of domestic government bond holdings is marked to
market in major emerging markets, exposing banks to market risk.
0
120
20
40
60
80
100
A haircut of about 30–40 percent would breach the minimum CET1
capital ratio in some regions.
5. Minimum Haircut of Government Securities to Generate a Bank
Capital Shortfall
(Percent of government securities holdings)
0
20
60
40
80
6. Banking Sector and Sovereign Credit Ratings
(Rating buckets, as of end of 2020)
Bank and sovereign credit ratings are closely tied.
AAA
BBB
A–
A+
AA
BB+
BB–
B
CCC+
CCC–
Sources: Bureau van Dijk’s Orbis; Cruces and Trebesch (2013); data compiled from banks’ accounting statements and Basel Pillar III disclosures; Fitch Connect;
Haver Analytics; Standard & Poor’s Capital IQ; IMF, Monetary and Financial Statistics database; and IMF staff calculations.
Note: In panel 5, the historical haircut corresponds to the average direct loss given default rates for sovereign debt holders across 68 economies during 1970–2010
as reported in Cruces and Trebesch (2013). The scenario haircut refers to the level of haircut to government securities that would breach the 4.5 percent minimum
CET1 capital ratio, assuming other sources of capital are unavailable. This is a strict approach since it is assumed that only the highest-quality capital is accessible.
The value of the haircut for each geographic region is computed as a median for banks in individual economies and over regions. In panel 6, bank credit ratings
correspond to the median rating across banks in each economy. The size of the dots is proportional to the size of the banking sector. Data labels use International
Organization for Standardization (ISO) country codes. CET1 = common equity Tier 1.
–60 40
RU
S
H
UN TU
R
ID
N
PO
L
BR
A
TH
A
CH
L
PH
L
ZA
F
CO
L
M
YS PE
R
H
UN PO
L
TU
R
BR
A
CO
L
ZA
F
PH
L
TH
A
IN
D
PE
R
M
YS
CH
L
RU
S
EM
s
CH
L
BR
A
RU
S
CO
L
TH
L
ID
N
M
YS PE
R
PO
L
PH
L
TU
R
H
UN EM
s
Ba
nk
r
at
in
gs
Sovereign rating
AAA AA A+ A– BBB BB+ BB– B CCC+ CCC–
Ch
an
ge
in
r
at
io
o
f l
oc
al
c
ur
re
nc
y
so
ve
re
ig
n
bo
nd
s
to
C
ET
1
ca
pi
ta
l
Change in loan-to-deposit ratio
–40 –20 0 20
POL
KAZ
MARRUS
VNM
GTM
JOR
CHN
MYS
PHL
THA
PER
URY
COL
IND
BRA

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
52 International Monetary Fund | April 2022
risks are examined for individual emerging markets,
while taking into account other domestic and exter-
nal factors that may impact these relationships.24
Three key findings emerge from this analysis.
First, the nexus is strong, on average, with signifi-
cant feedback effects between sectors (Figure 2.8).
Second, the strength of the transmission of risk
between sectors varies. For example, spillovers from
sovereign default risk to banks are, on average,
larger than those in the opposite direction from
banks to sovereign default risk. Overall, the largest
spillovers are from sovereign and bank default risk
to firms. Third, the relevance of the nexus differs
24To examine the relationships, a structural value-at-risk model is
estimated for 15 emerging markets using 2006–20 data; identi-
fication is achieved through Rigobon’s (2003) methodology. The
dependent variable is the expected default frequency (as a proxy for
default risk) for the sovereign, banking, and corporate sectors. See
Online Annex 2.5 for details on the empirical analysis.
across countries, with the transmission of shocks
being three to five times higher than the average in
some cases.
The heterogeneity in the size of the transmission
of shocks suggests that some country-specific factors,
such as the fiscal position and financial vulnerabili-
ties, may be at play in amplifying the impact of an
adverse shock. Further empirical analysis supports this
observation. For example, after a sharp tightening in
global financial conditions, emerging markets with
a higher level of public debt and banks’ holdings of
sovereign debt experience an increase in sovereign and
bank default risks that is twice as large as the average
increase (Figure 2.9).25 Furthermore, the impact of the
shock is persistent and remains larger than the average
effect for up to six quarters after the shock.
These findings confirm that the interlinkages
underlying the sovereign-bank nexus are relevant in
emerging markets. The next section further explores
these linkages and examines some of the key channels
and vulnerabilities that facilitate the transmission and
amplification of shocks across sectors.
Evidence about the Transmission Channels
To investigate the importance of the various transmis-
sion channels underlying the nexus in emerging markets,
this section focuses mainly on the direct shock trans-
mission from the sovereign sector to the banking and
corporate sectors. While shocks originating from banks
and firms may also be relevant, and may interact with a
sovereign shock, shock transmission from the sovereign
sector to the banking and corporate sectors appears to
be more pertinent at this juncture given the elevated
fiscal vulnerabilities in emerging markets that make the
sovereign particularly prone to an adverse shock.26
25For this exercise, a local projection panel regression model is
estimated to exploit the cross-country variation in vulnerabilities
using the same sample of countries and model specification as in
Figure 2.9. High levels of public debt and bank sovereign exposure
are defined as one standard deviation above the sample average
(equivalent to about 80 percent and 20 percent, respectively, while
the mean value is about 50 percent and 9 percent, respectively).
See Online Annex 2.5 for further details.
26As multiple channels of the nexus could operate simultaneously,
the analysis presented in the following sections is based on granular
bank- and corporate-level data to better identify the effects of each
individual channel. The results of these exercises, however, may not
be strictly comparable and are subject to some degree of estimation
uncertainty given that the sample composition varies across analyses,
depending upon data availability.
Estimated range
Average effect
St
an
da
rd
d
ev
ia
tio
n
Figure 2.8. Transmission of Risks through the
Sovereign-Bank Nexus: Strength of the Main Channels across
Emerging Markets
(Effect of a one standard deviation shock on other sectors’ default risk)
An increase in sovereign, bank, and nonfinancial corporation default risk
transmits across sectors with varying intensity.
–0.1
0.3
0.0
0.1
0.2
So
v

B
an
ks
So
v

N
FC
s
Ba
nk
s

S
ov
Ba
nk
s

N
FC
s
N
FC
s

S
ov
N
FC
s

B
an
ks
Sources: Haver Analytics; Moody’s; Refinitiv Datastream; and IMF staff
calculations.
Note: The figure shows the estimated range of coefficients for individual emerging
markets obtained from a structural model using daily data of default risk for
sovereign, banking, and corporate sectors. See Online Annex 2.5 for estimation
details. NFCs = nonfinancial corporations; Sov = sovereign.

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53International Monetary Fund | April 2022
Exposure Channel
As discussed, banks hold a substantial amount of
public debt, including as a share of capital, expos-
ing them to the risk of losses on these holdings.
Weaker capital buffers, in turn, can affect banks’
default risk and lending behavior. Empirical analysis
performed over a large sample of emerging market
banks using data for the past two decades confirms
this intuition.27 A sovereign distress event—defined
27The sample here comprises 525 banks based in 18 emerging
markets over 2000–20. The median credit default swap spread
in the sample is about 250 basis points. Banks’ indirect expo-
sure to changes in sovereign stress (such as through economic
growth, inflation, or exchange rate) is considered in the analysis
by including country-year fixed effects. Furthermore, to address
potential reverse causality concerns that sovereign distress in itself
may be driven by banking sector stress, alternative definitions of
sovereign distress—such as high government refinancing needs
during tight global financial market conditions, or large changes
in foreign-currency-denominated public debt due to currency
depreciation—are also considered for robustness. See Online
Annex 2.6 for details.
as an explicit default or a period with sovereign
credit default swap spreads higher than 500 basis
points—is followed within the same year by a
significant increase in default risk for banks with a
greater sovereign exposure. For instance, in the event
of sovereign distress, banks with a 10 percentage
point higher ratio of government debt holdings to
total bank assets (relative to average bank holdings
of government debt) face an expected default fre-
quency that is, on average, 0.4 percentage point
higher (Figure 2.10, panel 1, green bar). Notably, this
effect is about twice as large for banks with relatively
less capital (Figure 2.10, panel 1, red bar)28 and is
accompanied by a decline in their equity-to-assets
ratio (Figure 2.10, panel 2), presumably because more
exposed banks face higher funding costs that affect
their profits and equity.
28These effects appear meaningfully large, as the average expected
default frequency in the sample is 1.2 percent.
High public debt level
Average public debt level
High bank-sovereign exposure level
Average bank-sovereign exposure level
Figure 2.9. Sovereign and Bank Default Risk and Tightening of Global Financial Conditions in Emerging Markets
Sovereign default risk rises after global financial conditions tighten,
especially in emerging markets with higher public debt …
1. Cumulative Change in Sovereign Default Risk following a Global
Financial Conditions Shock
(Percentage points)
–0.06
–0.02
0.02
0.06
0.10
0 1 2 4 5 6 7 8 93 10
Quarters after the shocks
… and where banks have a higher sovereign exposure.
2. Cumulative Change in Bank Default Risk following a Global Financial
Conditions Shock
(Percentage points)
–2
–1
0
1
2
3
4
5
0 1 2 4 5 6 7 8 93 10
Quarters after the shock
Sources: Haver Analytics; Moody’s; Refinitiv Datastream; and IMF staff calculations.
Note: Panels 1 and 2 show results from local projection models in which the sovereign and banking default risks at quarterly frequency are regressed on lagged
values of each other, controlling for other domestic and external factors, including a global financial conditions index and its interaction with an indicator variable
identifying countries with high public debt or high bank-sovereign exposure (with high vulnerability identified as values of public debt to GDP or a ratio of banks’
holdings of government debt to total banking sector assets that is one standard deviation above the sample average). Solid dots indicate statistical significance at
10 percent or lower.

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
54 International Monetary Fund | April 2022
Banks with higher sovereign debt exposure also
cut back on lending more than their peers follow-
ing sovereign distress (Figure 2.10, panel 2). The
reduction in lending is consistent with losses from
sovereign debt exposures tightening banks’ capital
constraint and thus impairing their lending posture,
but it could also result from crowding-out effects,
which occur when banks lend more to the gov-
ernment at the expense of firms and households.
Empirical evidence supports this assertion: banks
with an average capital ratio that are more exposed
further increase their holdings of government debt
Average-capitalized banks Less-capitalized banks Change in equity
(percent)
Change in loans to assets
(percentage points)
Average-capitalized banks Less-capitalized banks
Change in equity to assets Change in loans to assets
1. Change in Banks’ Expected Default Frequency following Sovereign
Distress for Banks with Higher Sovereign Bond Holdings
(Percentage points)
2. Change in Bank Capital and Lending following Sovereign Distress
for Banks with Higher Sovereign Bond Holdings
(Percentage points)
3. Change in Bank Government Debt Holdings following Sovereign
Distress for Banks with Higher Sovereign Bond Holdings
(Percent)
4. Change in Equity and Loans following Sovereign Distress after an
Adverse External Shock
… and a further increase in banks’ government bond holdings.
Banks with greater sovereign debt holdings and weaker balance sheets
experience a higher default risk following sovereign distress …
… as well as lower capital and lending to the private sector …
Bank capital losses are significant following external shocks.
Sources: Bloomberg Finance L.P.; Fitch Connect; IMF, World Economic Outlook database; IHS Markit; Standard & Poor’s Capital IQ; and IMF staff calculations.
Note: Panels 1–4 report results from bank-level panel regressions. The dependent variable is the change in banks’ expected default frequency (panel 1); change in
equity to lagged total assets (panels 2 and 4, left side); change in total loans to total assets (panels 2 and 4, right side); and log change in total government debt
holdings (panel 3). Balance sheet variables and expected default frequency are based on year-end data. The focus variable is the ratio of banks’ holdings of
government debt securities to total assets (sovereign exposure) interacted with sovereign distress (or an alternative measure of sovereign stress in panel 4) and the
bank capital ratio (total-equities-to-total-assets ratio). The average effect refers to the impact of 10 percentage point higher bank sovereign exposure on the
dependent variable for banks with an average capital ratio (which is close to a one standard deviation in the sample). The impact of “less-capitalized” banks
corresponds to a bank capital ratio one standard deviation below the mean. Sovereign distress indicates periods when the monthly average of sovereign credit
default swap spreads is higher than 500 basis points within a given year, or Standard & Poor’s long-term rating for sovereign foreign exchange debt is CCC– or
lower, or the government is in external or domestic default according to Harvard Business School Global Crises Data by Country. In panel 4, the valuation effect on
public debt following a currency depreciation is computed by multiplying foreign-currency-denominated gross public debt in year t −1 by the change in the exchange
rate from t −1 to t. The valuation effect is then normalized by total gross public debt in t −1. Solid bars indicate statistical significance at 10 percent or lower. See
Online Annex 2.6 for further details. VIX = Chicago Board Options Exchange Volatility Index.
Figure 2.10. Transmission of Sovereign Risk through the Exposure Channel
0.0
0.8
0.2
0.4
0.6
0
16
2
4
6
8
10
12
14
–15
–10
–5
0
–3
–2
–1
0
Average-capitalized banks Less-capitalized banks
Following an increase in
public debt due to currency
depreciation
Following an increase in
VIX in economies with higher
expected maturing debt

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55International Monetary Fund | April 2022
when the sovereign is in distress (Figure 2.10,
panel 3).29,30
The effects on default risk, bank lending, and
capitalization tend to grow in magnitude as sover-
eign distress deepens, pointing to possible nonlinear
effects. Thus, for example, the impact of sovereign
distress on banks’ equity is more than twice as large
when sovereign spreads reach 1,000 basis points
(Online Annex 2.6). The sovereign’s holdings of
international reserves act as a buffer, helping to
dampen the severity of the shock. On average,
domestic banks in countries with a higher stock
of foreign exchange reserves relative to short-term
external debt experience a significantly smaller
decline in capital during episodes of intense sover-
eign stress than domestic banks in countries with
less adequate reserves (Online Annex 2.6), possi-
bly because of a smaller currency depreciation and
more limited funding cost increases from unhedged
foreign debt.
The analysis also considers the impact of an
increase in sovereign risk associated with a tightening
in global financial conditions by focusing on two
alternative definitions of sovereign distress. The first is
defined as a situation in which sovereign debt rollover
needs are high amid significant volatility in global
financial markets. The second is an episode in which
public debt increases sharply following a currency
depreciation. In most of these cases the impact on
banks’ equity and loans is significantly larger than
in cases of low fiscal vulnerabilities following the
external shocks (Figure 2.10, panel 4). These findings
confirm the relevance of the exposure channel in
emerging markets and highlight the amplification of
the nexus when fiscal, financial, and external vulner-
abilities are high and external financial conditions
deteriorate.
29Intuitively, it could be that banks are forced to hold more
sovereign debt, since sovereign refinancing needs are typically higher
during sovereign distress. But banks may also extend less credit
to the private sector during such episodes because of weak credit
demand, which is captured by including country-year effects in
the regression.
30The effects documented in Figure 2.10 (panels 2 and 3) are
robust to defining the dependent variables as percentage changes in
bank equity and lending, and the results are similar to those reported
in the literature on the euro area sovereign debt crisis (Acharya and
others 2018; Bofondi, Carpinelli, and Sette 2018).
Safety Net Channel
Risks to the banking sector are also intertwined
with sovereign risks through the explicit and implicit
guarantees, or the safety net, provided by the sovereign
to banks. To assess the transmission of shocks through
this channel, the analysis relies on bank-level estimates
of government support called support rating floors—
developed by the Fitch rating agency—which isolate
potential sovereign support for banks from other
sources of external support.31 On average, government
support proxied through the support rating floors is
greater in emerging markets than in advanced econ-
omies, and it has generally increased since the global
financial crisis (Figure 2.11, panel 1).32
The extent to which banks benefit from the pub-
lic safety net varies across emerging markets and is
importantly associated with bank-specific characteris-
tics (Online Annex 2.7).33 In general, there is a strong
positive relationship between bank size and govern-
ment support ratings, implying large implicit subsidies
for banks that are “too big to fail.” In addition, banks
with higher support rating floors tend to have lower
capital ratios (Online Annex Figure 2.7.4, panel 2)—
pointing to potential moral hazard—and a majority
government stake.
This safety net provides some protection to banks
and their performance in times of financial stress.
However, when the sovereign itself is under stress,
the perception of a weaker ability to support banks
could undermine investor confidence and banks’
performance. This indeed appears to be the case: the
31The indicator reflects the Fitch rating agency’s judgment of
the propensity and ability of a government to provide support to a
bank. Factors used to assess the support rating floor include the size
and structure of the banking system, sovereign financial flexibility,
resolution legislation, support stance, bank systemic importance,
bank liability structure, bank ownership, policy role, guarantees, and
legal status. The key advantage of this indicator is that it does not
incorporate other forms of external support, such as the institutional
support of the entity’s shareholders. The rating also does not reflect
the intrinsic credit quality of the bank.
32The contrasting patterns between advanced economies and
emerging markets may reflect different implementation stages of
their regulatory reforms (for example, capital surcharges for global
systemically important banks). The correlation between bank size
and the support rating floor in advanced economies has diverged
from that in emerging markets and has substantially receded since
the end of 2015, just before the capital surcharges for global systemi-
cally important banks were phased in.
33The distribution of government support ratings spans a wide
spectrum in emerging markets, ranging from high to no support, but
has changed little since 2007 (see Online Annex 2.7).

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56 International Monetary Fund | April 2022
20202007
Normal times
Sovereign stress (low public debt)
Sovereign stress (high public debt)
Normal times
Sovereign distress (average effect)
Sovereign distress (less-capitalized banks)
Normal times
Sovereign distress (average effect)
Sovereign distress
(less-capitalized banks)
1. Average Bank Government Support Ratings across Emerging
Markets
(Support rating floor on a numerical scale from 0 to 17)
2. Cumulative Abnormal Returns of Banks with a One-Notch-Higher
Government Support Rating in Countries with Different Public Debt
Levels
(Percentage points)
Government implicit guarantees to the banking sector have increased
since the global financial crisis.
Government guarantees support banks after sovereign distress, but not
so much in countries with high public debt.
Sources: Fitch Connect; IHS Markit; Refinitiv Datastream; Standard & Poor’s Capital IQ; and IMF staff calculations.
Note: Panel 1 shows the weighted average of Fitch support rating floors in major emerging markets, in which weights correspond to banks’ total assets in US dollars.
The support rating floor ranges from AAA to NF and is converted to a numerical scale of 1–17 (higher values correspond to a higher rating or higher likelihood of
receiving government support during distress). Panel 2 shows the capital asset pricing model-based cumulative abnormal returns associated with a one-notch-
higher support rating floor after sovereign distress using a local projection methodology. Sovereign distress indicates the months with average sovereign credit
default swap spreads higher than 500 basis points, a Standard & Poor’s long-term rating for sovereign foreign exchange debt that is CCC– or lower, or months with
external or domestic debt defaults occurred. Estimated abnormal returns are shown for economies with a sovereign-debt-to-GDP ratio greater than 60 percent (“high
public debt”) or lower than 60 percent (“low public debt”). Panel 3 shows cumulative bank credit growth associated with a one-notch-higher support rating floor up
to five years after the sovereign distress or during normal times. The green line shows the impact after the sovereign distress for banks with an average
equity-to-capital ratio, while the red line shows the cumulative impact following the same sovereign distress but for banks with an equity-to-capital ratio that is one
standard deviation below average. Panel 4 shows results for a similar analysis in which the dependent variable is the cumulative increase in the bank nonperforming-
loans-to-assets ratio. In panels 1–4, the analysis is based on the sample of firms with available support rating floor information. Solid dots indicate statistical
significance at 10 percent or lower. Data labels use International Organization for Standardization (ISO) country codes. AEs = advanced economies; EMs = emerging
markets.
Figure 2.11. The Banking Sector Safety Net in Emerging Market Economies
0
10
2
4
6
8
–40
10
–30
–20
–10
0
CH
N
TH
A
PH
L
IN
D
ID
N
ZA
F
RU
S
BR
A
EG
Y
M
YS
TU
R
M
EX
EM
s
EM
s
(e
xc
l.
CH
N
)
AE
s
3. Cumulative Bank Credit Growth with a One-Notch-Higher
Government Support Rating across Banks with Different Capital
Buffers
(Percentage points)
4. Cumulative Change in Bank Nonperforming Loan Ratio with a
One-Notch-Higher Government Support Rating across Banks with
Different Capital Buffers
(Percentage points)
Undercapitalized banks with higher implicit guarantees increase credit
growth following sovereign distress …
… leading to higher levels of nonperforming loans, suggesting
increased risk-taking.
–25
10
–20
–15
–10
–5
0
5
–4
6
–2
0
2
4
Years after sovereign distress
0 1 2 3 4 5
Years after sovereign distress
0 1 2 3 4 5
Months after sovereign distress
0 1 2 3 4 5 6 7 8 9 10 11 12

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57International Monetary Fund | April 2022
equity returns of emerging market banks in times of
sovereign distress are higher for banks whose support
rating floor is one notch higher than that of their peers
( Figure 2.11, panel 2), whereas in normal times there
is no significant difference between the two groups.34
However, the positive effect of higher implicit guaran-
tees before sovereign distress declines over time, turn-
ing negative six months after the shock—potentially
suggesting that the weakened sovereign strength
eventually hurts the credibility of these guarantees.
Accordingly, the negative effect on banks with high
government support ratings starts sooner and is larger
if the economy enters the distress event with a higher
public debt burden (Figure 2.11, panel 2, red line).
The strength of sovereign support also matters for
the ability of banks to lend following a sovereign
distress event. Banks with higher government support
ratings experience lower credit growth, particularly
after three years (Figure 2.11, panel 3, green line),
which is in line with the negative impact on bank
stock returns observed after the sovereign distress
event. Furthermore, banks with a higher support rating
floor but lower capital expand their loan portfolios
more aggressively, with cumulative credit growth
about 8 percentage points higher than that of other
banks two years after the distress event (Figure 2.11,
panel 3). This increase in lending goes hand in hand
with a worsening of bank credit quality, which sug-
gests greater risk-taking by these banks. For example,
although nonperforming loans do not seem to depend
much on the level of the government support rating
on average, banks with both a lower capital ratio
and a higher support rating experience a significant
jump in nonperforming loans in the medium term
(Figure 2.11, panel 4).
Macroeconomic Channel
Empirically analyzing the macroeconomic channel—
that is, the interconnectedness of sovereigns and banks
through the real economy—is particularly challenging
because of difficulties in isolating shocks to different
sectors (Dell’Ariccia and others 2018).35 For simplicity,
the following analysis focuses on one component of
34The sample for this analysis is composed of 10 major emerging
markets covering the period 2007–20. See Online Annex 2.7 for
further details of the empirical analysis.
35For example, sovereign and corporate riskiness may be influ-
enced by common factors, such as a decline in economic activity.
this channel: the transmission of risk from the sover-
eign to the corporate sector.
A possible empirical strategy to identify the effect
of a rise in sovereign risk on firms is to exploit the
uneven effect of sovereign downgrades on firms with
different credit ratings. While downgrades of firms
and sovereigns may both be driven by a deterioration
in economic fundamentals, sovereign downgrades are
more likely to cause the downgrades of highly rated
firms because of rating agencies’ ceiling policies. These
policies often require that firms’ ratings remain at or
below the sovereign rating of their country of domi-
cile.36 This approach allows the analysis in turn to
isolate the direct effect of a sovereign downgrade on
firms by comparing the performance of firms subject
to ceiling policies (“bound firms”—that is, those with
a rating equal to or above that of the sovereign) with
that of firms not subject to these policies (“unbound
firms”—that is, those with a lower rating than the sov-
ereign) under the assumption that both groups of firms
are equally affected by the change in fundamentals.37
The data confirm that the ratings of bound firms
are more affected by sovereign downgrades than the
ratings of unbound firms (Figure 2.12, panel 1).38 A
formal analysis of the two groups of firms following a
sovereign downgrade shows that a bound firm’s cumu-
lative investment drops nearly 17 percentage points
more than an unbound firm’s cumulative investment
(controlling for firm characteristics) two years after a
sovereign downgrade (Figure 2.12, panel 2). Further-
more, the effect on investment is significantly larger if
the sovereign downgrade is accompanied by higher sov-
ereign stress, proxied by sovereign credit default swap
spreads greater than 500 basis points (Figure 2.12,
panel 3). Overall, these results are consistent with the
36These policies are set after taking into account the risk of capital
and foreign exchange controls, which could hamper a firm’s ability to
service its debt. A similar empirical strategy is used in Almeida, Fos,
and Kronlund (2016).
37It is worth noting that unbound firms are by definition those
with lower credit quality than bound firms. Thus, a key advantage
of this empirical approach is that alternative explanations based
on changes in fundamentals and credit risk are unlikely to explain
the differential impact on firms’ performance around the sover-
eign ceiling.
38The sample is composed of 100 sovereign debt downgrades
in 29 countries during 1998–2020. For each country, years with
banking crises in which the country was downgraded are excluded in
order to better isolate the direct real effect of sovereign downgrades
(Almeida, Fos, and Kronlund 2016). See Online Annex 2.8 for
further estimation details.

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58 International Monetary Fund | April 2022
Bound
Non-bound
Figure 2.12. The Effects of Sovereign Downgrades on Firms
1. Distribution of the Change in Firms’ Ratings following a Sovereign
Downgrade
(Density)
The ratings of bound firms have a higher probability of being
downgraded after a sovereign downgrade …
Cumulative rating change two years after sovereign downgrade
0
0.1
0.2
0.3
0.4
0.5
–1
2
–1
1
–1
0 –9 –8 –7 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5
2. Effect of a Sovereign Downgrade on Investment and Debt Issuance
(Percentage points)
… and bound firms reduce their investment and debt issuance more
than unbound firms.
–17.0
–16.5
–16.0
–15.5
–15.0
Investment Debt issuance
3. Effect of a Sovereign Downgrade on Investment following a
Sovereign Downgrade at Different Levels of Sovereign Risk
(Percentage points)
The impact is larger when the downgrade is accompanied by sovereign
distress …
–100
–80
–60
–40
–20
0
20
40
Low sovereign risk High sovereign risk
4. Additional Change in the Nonperforming Loan Ratio after a
Sovereign Downgrade in Countries with a Higher Share of Bound
Firms in the Corporate Sector
(Percentage points)
… and leads to spillover effects on banks’ asset quality.
0 1 2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Years from the sovereign downgrade
Sources: Haver Analytics; IHS Markit; Standard & Poor’s Capital IQ; and IMF staff calculations.
Note: Panel 1 shows the distribution of the change in corporate ratings between the period before the sovereign downgrade and two years after the downgrade for
“bound” and “unbound” firms. Bound firms are those with a rating equal to or above their sovereign before the downgrade. Panel 2 reports the estimates based on a
difference-in-differences model comparing changes in the outcome variable between bound and unbound firms around the sovereign downgrade, in which the
considered outcome variables are the changes in the firm’s investment ratio and debt issuance between the period before the sovereign downgrade and two years
later. The investment ratio is equal to the ratio of capital expenditure to lagged capital stock. Debt issuance is proxied by changes in the net-debt-issuance-to-asset
ratio. Panel 3 shows the marginal effect of a sovereign downgrade on bound firms for different levels of sovereign risk. Low sovereign risk refers to periods with a
sovereign credit default swap (CDS) spread between 250 and 500 basis points. High sovereign risk refers to periods with a sovereign CDS spread greater than 500
basis points. Panel 4 shows the cumulative effect of a one standard deviation larger share of assets of bound firms in economy-wide corporate assets on the change
in banking sector nonperforming loans ratio two years after the sovereign downgrade. Estimates are based on a country-level difference-in-differences model. Solid
bars indicate statistical significance at 10 percent or lower. See Online Annex 2.8 for further details of the empirical analysis.

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59International Monetary Fund | April 2022
hypothesis that firms face tighter funding constraints
when directly affected by a sovereign downgrade.
The negative effects of sovereign stress on firms’
borrowing costs and activity may weaken the sound-
ness of their balance sheets. Consequently, banks’ loan
portfolio quality may be adversely affected, possibly
leading them to curtail lending. This would further
reduce consumption and investment in the domes-
tic economy, with a consequent drop in aggregate
demand and decline in the health of the corporate
sector. Hence, disruptions in financial intermediation
could act as an amplifier and exacerbate the damage
to economic activity following a sovereign downgrade.
Empirical evidence supports this intuition: following
a sovereign downgrade, banks’ nonperforming loans
increase more in economies where bound firms play a
larger role in the corporate sector, as determined by the
share of their assets in total economy-wide corporate
assets (Figure 2.12, panel 4).39
Conclusion and Policy Recommendations
The sovereign-bank nexus has intensified in emerg-
ing markets as banks’ exposure to domestic sovereign
debt has increased to all-time highs. With public debt
also historically high—and with the sovereign credit
outlook deteriorating in many emerging markets—it
is increasingly likely that a negative shock to the
sovereign balance sheet may trigger an adverse feed-
back loop between sovereigns and banks that could
threaten macro-financial stability. The analysis in this
chapter shows that such a loop could occur through
multiple channels, including by affecting corporate
sector activity, and would be stronger in countries with
higher fiscal vulnerabilities and less-well-capitalized
banking systems.
Emerging markets thus face complex policy
trade-offs amid tighter global financial conditions
on the back of monetary policy normalization in
advanced economies and heightened economic and
39These findings are based on a country-level difference-in-differences
regression, in which banking sector nonperforming loans across coun-
tries are regressed on the share of bound firms’ assets relative to total
assets of the nonfinancial corporate sector, and other control variables
(see Online Annex 2.8). The results indicate that a one standard devi-
ation higher value of this share is associated with a 1 percentage point
greater change in nonperforming loans two years after the sovereign
downgrade. However, these findings are only suggestive—a more direct
analysis linking banks’ lending behavior to their exposure to bound
firms is difficult given a lack of available data.
geopolitical uncertainty. Growth prospects are weak in
several emerging markets; policy space to support the
economy is limited, and borrowing constraints have
tightened as foreign investor interest in local currency
sovereign bond markets has dwindled and yields have
risen. Policymakers must remain vigilant to emerging
signs of vulnerability in the banking sector and ensure
banking sector stability in the event of deteriorating
credit quality.
Given the strength and multifaceted nature of the
sovereign-bank nexus, policy action is required on mul-
tiple fronts. Given the heterogeneity of countries’ fiscal
and financial vulnerabilities, policy must be tailored to
country-specific circumstances. In general, countries
with stronger fiscal positions and a sound banking
system will be better placed to manage tighter financial
conditions. But they should seek to extend matur-
ities of public debt where feasible and avoid a further
buildup of currency mismatches to limit balance sheet
vulnerabilities (see the January 2022 World Economic
Outlook Update). In countries with limited fiscal space
and tight borrowing constraints, it is imperative to
(1) improve the efficiency and targeting of fiscal spend-
ing to support recovery and (2) embed fiscal policy
in credible and sustainable medium-term fiscal plans
to mitigate the impact of an adverse shock (see the
April 2022 Fiscal Monitor). Some emerging markets—
especially those with larger maturing debt or higher
exposure to exchange rate volatility—may need to
adjust faster to preserve market confidence and prevent
a further intensification of the sovereign-bank nexus.
Policymakers should also seek to develop robust
resolution frameworks for sovereign debt to facilitate
orderly deleveraging and restructuring if needed (IMF
2020a). Domestic debt restructurings may become
more frequent in the future following the increase
in the share of domestic debt in total public debt in
emerging markets, so a sovereign considering such
restructuring should anticipate, minimize, and manage
its impact on the financial system and broader econ-
omy (IMF 2021).
On the financial sector front, banks’ resources should
be preserved to absorb potential losses by limiting capital
distribution in cases where bank profitability is difficult
to assess because of regulatory flexibility. Fully assessing
banking sector health remains difficult in many coun-
tries due to regulatory flexibility and forbearance. As a
result, asset quality reviews may be necessary to quantify
hidden losses and identify weak banks once forbearance

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60 International Monetary Fund | April 2022
has ceased. The results of these reviews may guide
supervisory actions requiring more robust levels and
quality of bank capital, which could be phased in over
time in a preannounced manner to minimize procyclical
effects. This is especially pertinent for countries with
weak growth prospects and high corporate insolvency
risks that could adversely affect financial stability should
banks ultimately need to recognize loan losses. Moreover,
in emerging markets with inadequate frameworks to
deal with corporate bankruptcies, private debt resolution
frameworks should be strengthened to prepare for the
eventual withdrawal of policy support measures and
minimize risks to macro-financial stability.40
Risk to banks from sovereign exposure can mate-
rialize not just in emerging markets but also in more
advanced economies, as was the case in Europe
following the global financial crisis. Hence, improving
transparency and data quality of banks’ holdings of
government debt to assess risks arising from possible
sovereign distress should be a global priority. While
current international standards stop short of “encour-
aging” banks to disclose data on all material sovereign
exposures by currency denomination and account
classification (BCBS 2021), market discipline will
work meaningfully only if this becomes a necessary
requirement for all banks. Furthermore, banks could
be required to cover the risks of significant sovereign
exposures in their stress tests by taking into account
the multiple channels of the nexus.41
Once the economic recovery has taken hold and
pandemic-related financial sector support measures
have been normalized, both advanced and emerging
market economies could consider introducing measures
aimed at reducing incentives to hold excessive sover-
eign debt.42,43 In this regard, several reform options
40Liu, Garrido, and DeLong (2020) discuss in detail the key mea-
sures needed for effective private sector debt resolution.
41See Jobst and Oura (2019) for recent approaches to stress testing
sovereign exposures.
42Sovereign debt exposures could become excessive if banks are
not fully pricing the risks associated with them, expecting to be
bailed out in the event of sovereign distress (Dell’Ariccia and others
2018; Farhi and Tirole 2015). Furthermore, the expectation of inter-
vention might lead to correlated risk exposures across banks as banks
expect public support to be more likely in a systemic banking crisis.
43In the current regulatory framework, sovereign exposures are
treated more favorably than other asset classes, encouraging banks to
hold sovereign bonds. The Basel Committee’s standardized approach to
credit risk provides a regulatory exemption that allows banks to apply
zero risk weights on local currency government bonds regardless of
sovereign risk. Other aspects of the regulatory framework, such as the
liquidity standards, also favor the holding of sovereign debt.
have been discussed internationally in the aftermath of
the global financial crisis, including the establishment
of nonzero, risk-sensitive capital requirements (BCBS
2017). So far, however, no consensus has been reached
to make any changes to the regulatory capital treat-
ment of risks from sovereign exposures, although the
Basel Committee could consider resuming its efforts
in this regard. An alternative approach could be strict
concentration limits, but these are likely to generate
negative effects because banks need to hold sovereign
bonds for liquidity management. Capital surcharges
on bank holdings of domestic sovereign bonds above
certain thresholds are more flexible and can target
concentration risk if appropriately calibrated. The set-
ting of such a surcharge should consider the liquidity
needs and availability of other liquid assets in domestic
currency, along with the perceived risk from excessive
concentration.44
Strengthening banking crisis management frame-
works could reduce the need for government guar-
antees and minimize the costs of resolution to the
government, including through the recovery of public
funds from the industry. Some emerging markets
have made much progress in this regard (Botes and
others 2021). Given the economic uncertainty and
the eventual unwinding of financial sector measures
that have supported bank balance sheets through the
pandemic, it is important to act to strengthen the
financial safety net, including through deposit guar-
antee programs, resolution regimes, and central bank
liquidity facilities. Preparing contingency plans that
detail how the authorities will respond to possible
future pressures is critical to support effective policy
responses should an adverse scenario materialize
(IMF 2020b).
Effective governance, regulation, and supervision
are necessary to ensure that public banks are safe and
sound while achieving their public policy objectives
(IMF, forthcoming). Mitigating the risks to financial
stability posed by public banks requires closing existing
prudential gaps. Deposit-taking public banks directly
competing with private banks should be subject to the
same expectations and requirements of governance,
44The IMF’s Financial Sector Assessment Program for Romania
provides an example of systemic risk buffer calibration that aims to
ensure the resilience of banks with concentrated exposures, while
minimizing potential adverse impacts (IMF 2018). The framework
applies a marginal scheme, with systemic risk buffer surcharges rising
with the ratio of sovereign exposures to risk-weighted assets.

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61International Monetary Fund | April 2022
disclosure, regulation, and supervision as private banks.
A key element of the reform agenda should be to
promote mechanisms so that arm’s length distance can
be created between the government as the owner and
the management of the bank, which can then run the
bank on as much a commercial basis as possible. The
government’s role as an informed owner should also be
separated from the supervisory authority’s prudential
supervision role.
Given that a lack of investor diversity can induce
volatility in sovereign debt markets amid sudden
changes in risk appetite, policymakers should aim
to promote a deep and diversified investor base to
strengthen market resilience in countries with under-
developed local currency bond markets (IMF 2021).
While domestic banks usually play a major role in
emerging market and developing economies both as
investors in government bonds and as intermediaries
for government bond trading, a highly concentrated
banking sector can undermine banks’ incentives to
trade and can impede market liquidity.45 A developed
investor base should thus include a diverse range of
bank and nonbank participants with different invest-
ment horizons and risk-return preferences, particu-
larly institutional investors, to allow the government
to spread risk in its debt portfolio and extend the
yield curve.46 This would also help mitigate banks’
excessive exposure to the sovereign and weaken the
sovereign-bank nexus.
45Banks tend to trade securities for liquidity management
purposes, which helps bolster secondary market activity. A highly
concentrated banking sector can restrict market liquidity in countries
with smaller financial systems.
46Nonbank investors bring different risk-return preferences and
investment horizons to the government bond market compared
with banks. For example, pension funds and insurance companies
generally prefer longer-dated assets to match their longer-term
liabilities, largely determining the ability of the government to issue
longer-dated securities and thereby facilitating the extension of the
yield curve. See IMF (2021) for detailed guidance on diversifying
the investor base and developing local currency bond markets in
emerging market and developing economies.

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62 International monetary Fund | April 2022
Bank holdings of sovereign debt vary significantly
across emerging markets, ranging from about 5 per-
cent of banking sector assets (for example, in Chile
and Peru) to more than 25 percent (for example, in
Brazil and Pakistan) (Figure 2.1.1). In general, the
exposure of emerging market banks to sovereign debt
has risen since the global financial crisis, most notably
in China, Hungary, and Pakistan.
Why do banks hold government debt? Several
factors may be at play, including liquidity man-
agement, expected returns, and limited alternative
investment opportunities (Dell’Ariccia and others
2018). Sovereign debt offers a relatively liquid and
safe asset status that may be particularly attractive
in countries with weaker institutions and enforce-
ment of creditor rights that could lower incentives
for banks to lend to the private sector (Holmström
and Tirole 1998). Banks may serve as market mak-
ers in government bond markets, while their gov-
ernment bond holdings also serve as collateral for
securing funding from the central bank. The regula-
tory treatment of sovereign exposures—which allows
banks to apply zero risk weights on local currency
domestic government bonds—also makes them
attractive for banks to hold. Moral suasion and
risk shifting are two other potential reasons. Moral
suasion refers to government pressure on banks to
purchase public debt; risk shifting can occur during
times of sovereign distress when banks increase their
sovereign debt exposure to take advantage of higher
sovereign yields.1
For emerging markets, empirical analysis
using country-level data shows that several of the
abovementioned factors are relevant (Figure 2.1.2,
panel 1).2 For example, banks tend to hold more
government debt when interest rates are high and the
sovereign is more indebted (pointing perhaps to moral
suasion or risk-shifting motives) and when there are
fewer opportunities to lend to the private sector, as
indicated by a lower ratio of stock market capitaliza-
tion to GDP, as well as a lower ratio of private sector
credit to GDP.
The author of this box is Tara Iyer.
1The flip side of this is that during sovereign distress, domestic
banks could incur huge losses that wipe out their capital, leading
to a banking crisis.
2See Online Annex 2.4 for a detailed description of the model,
estimation method, and data used for this analysis.
Further analysis using bank-level data shows
that moral suasion and risk-shifting motives are
indeed important in emerging markets. Domestic
state-owned banks, generally dominant in emerging
markets and potentially more likely to be induced
to hold government debt (Ongena, Popov, and Van
Horen 2019),3 purchase significantly more sovereign
debt in times of high fiscal need or when the sover-
eign is in distress (Figure 2.1.2, panel 2).4 However,
3Domestic state-owned banks tend to be generally dominant
in emerging markets. On average, such banks held about 30 per-
cent of total banking sector assets in major emerging markets in
2020, but this ratio exceeded 40 percent in some countries.
4High fiscal need is defined as years when maturing sovereign
debt (to lagged total debt) is in the top 75th percentile of the
distribution, indicating that more new public debt is likely
to be issued. Sovereign distress is defined as periods when
the sovereign credit default spread exceeds 500 basis points,
a Standard & Poor’s long-term rating for sovereign foreign
currency debt CCC – or lower, or the sovereign is in external or
domestic default.
2018–20 2008–10
Figure 2.1.1. Bank Holdings of Sovereign Debt
(Percent of total bank assets)
0
40
5
10
15
20
25
30
35
Pa
ki
st
an
Br
az
il
Ar
ge
nt
in
a
H
un
ga
ry
In
di
a
M
ex
ic
o
Po
la
nd
Tu
rk
ey
Ph
ili
pp
in
es
So
ut
h
Af
ric
a
Ch
in
a
M
al
ay
si
a
In
do
ne
si
a
Bu
lg
ar
ia
Co
lo
m
bi
a
Th
ai
la
nd
Pe
ru
Ch
ile
Sources: Fitch Connect; IMF, Monetary and Financial Statistics
database; and IMF staff calculations.
Note: Given limited country-level data availability, banks’
sovereign debt exposures for India and Argentina are computed
using bank-level Fitch Connect data.
Box 2.1. The Drivers of Banks’ Sovereign Debt Exposure in Emerging Markets

C H A P T E R 2 T h E S O v E R E I G N – B A N k N E x U S I N E M E R G I N G M A R k E T S : A R I S k Y E M B R A C E
63International Monetary Fund | April 2022
there is no such evidence of government pressure
on private banks (Online Annex 2.4). Moreover,
less-capitalized state-owned banks are more likely to
purchase sovereign debt during periods of sovereign
distress (Figure 2.1.2, panel 2). This pattern suggests
the presence of a moral suasion motive, but there
may also be a risk-shifting strategy by these banks,
whereby they are more willing to take on additional
risk and improve their capital positions by purchasing
high-yield debt (Acharya and others 2018).
High sovereign stress Full sample
Additional purchase during periods of high fiscal need
Additional purchase by less-capitalized banks
Figure 2.1.2. Drivers of Bank Holdings of Sovereign Debt in Emerging Markets
Banks hold more sovereign debt in more indebted and
less financially developed economies.
1. Drivers of Bank Holdings of Sovereign Debt
(Percentage points)
–6
6
–4
–2
0
2
4
Gross public
debt
Interest rate Stock market
capitalization
Credit to the
private sector
State-owned banks are subject to moral suasion and
engage in risk shifting.
2. State-Owned Banks: Net Purchase of Sovereign Bonds
during Periods of Sovereign Distress
(Percent)
0
30
5
10
15
20
25
Sources: Bloomberg Finance L.P.; Fitch Connect; IHS Markit; IMF, Monetary and Financial Statistics and World Economic Outlook
databases; Standard & Poor’s Capital IQ; and IMF staff calculations.
Note: Panel 1 presents results obtained from a cross-country regression for a sample of 21 emerging markets during 2000–20.
Aggregate banks’ government debt holdings are computed from Fitch Connect if data from Monetary and Financial Statistics are
limited. The dependent variable is banks’ holdings of sovereign debt to total banking sector assets. The bars show the effect of a one
standard deviation increase in the value of the regressors on changes in banks’ holdings (in percentage points). Panel 2 presents
regression results from a bank-level cross-country regression during 2011–20. The dependent variable is banks’ net purchases of
sovereign debt. (See Online Annex 2.4 for the model and estimation details.) Moral suasion is defined as the additional purchase of
sovereign debt by state-owned banks in times of “high fiscal need”; that is, the years when the total amount of new debt auctioned by
the sovereign (proxied by maturing debt as a share of lagged gross debt) is above the 75th percentile in the sample. Risk shifting is
defined as the additional purchases of sovereign debt by less-capitalized state-owned banks, where “less capitalized” refers to an
equity-to-assets ratio that is one standard deviation below the mean, which is about 7 percentage points. Solid bars indicate statistical
significance at 10 percent or lower.
Box 2.1 (continued)

G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
64 International Monetary Fund | April 2022
References
Acharya, Viral V., Tim Eisert, Christian Eufinger, and Christian
Hirsch. 2018. “Real Effects of the Sovereign Debt Crisis in
Europe: Evidence from Syndicated Loans.” Review of Financial
Studies 31 (8): 2855–96.
Almeida, Heitor, Vyacheslav Fos, and Mathias Kronlund. 2016.
“The Real Effects of Share Repurchases.” Journal of Financial
Economics 119 (1): 168–85.
Arslanalp, Serkan, and Takahiro Tsuda. 2014. “Tracking Global
Demand for Advanced Economy Sovereign Debt.” IMF
Economic Review 62 (3): 430–64.
Asonuma, Tamon, Said Bakhache, and Heiko Hesse. 2015. “Is
Banks’ Home Bias Good or Bad for Public Debt Sustain-
ability?” IMF Working Paper 15/44, International Monetary
Fund, Washington, DC.
Basel Committee on Banking Supervision (BCBS). 2017. “The
Regulatory Treatment of Sovereign Exposures.” Discussion
Paper, Basel.
Basel Committee on Banking Supervision (BCBS). 2021.
“Voluntary Disclosure of Sovereign Exposures.” Basel.
Bofondi, Marcello, Luisa Carpinelli, and Enrico Sette. 2018.
“Credit Supply during a Sovereign Debt Crisis.” Journal of the
European Economic Association 16 (3): 696–729.
Botes, Jacques, Aidan Lawson, Vasily Pozdyshev, and Rastko
Vrbaski. 2021. “Managing Banking Crises in Emerging
Market Economies.” FSI Insights on Policy Implementa-
tion 38, Financial Stability Institute, Bank for International
Settlements, Basel.
Cruces, Juan J., and Christoph Trebesch. 2013. “Sovereign
Defaults: The Price of Haircuts.” American Economic Journal:
Macroeconomics 5 (3): 85–117.
Dell’Ariccia, Giovanni, Caio Ferreira, Nigel Jenkinson, Luc
Laeven, Alberto Martin, Camelia Minoiu, and Alex Popov.
2018. “Managing the Sovereign-Bank Nexus.” ECB Working
Paper 2177, European Central Bank, Frankfurt.
European Central Bank (ECB). 2020. Financial Stability Review
November 2020. Frankfurt.
Farhi, Emmanuel, and Jean Tirole. 2015. “Liquid Bundles.”
Journal of Economic Theory 158:634–55.
Feyen, Erik, and Igor Esteban Zuccardi Huertas. 2019. “The
Sovereign-Bank Nexus in EMDEs: What Is It, Is It Rising,
and What Are the Policy Implications?” Policy Research
Working Paper 8950, World Bank, Washington, DC.
Gennaioli, Nicola, Alberto Martin, and Stefano Rossi. 2018.
“Banks, Government Bonds, and Default: What Do the Data
Say?” Journal of Monetary Economics 98:98–113.
Holmström, Bengt, and Jean Tirole. 1998. “Private and Public Sup-
ply of Liquidity.” Journal of Political Economy 106 (1): 1–40.
International Monetary Fund (IMF). 2018. “Romania: Financial
Sector Assessment Program.” June 8. https:// www .imf .org/
en/ Publications/ CR/ Issues/ 2018/ 06/ 08/ Romania -Financial
-Sector -Assessment -Program -45961.
International Monetary Fund (IMF). 2020a. “The Interna-
tional Architecture for Resolving Sovereign Debt Involving
Private-Sector Creditors—Recent Developments, Challenges, and
Reform Options.” IMF Policy Paper 2021/071, Washington, DC.
International Monetary Fund (IMF). 2020b. “Managing Sys-
temic Banking Crises: New Lessons and Lessons Relearned.”
Departmental Paper 20/05, Washington, DC.
International Monetary Fund (IMF). 2021. “Issues in Restruc-
turing of Sovereign Domestic Debt.” IMF Policy Paper
2020/043, Washington, DC.
International Monetary Fund (IMF). 2022. “South Africa: 2021
Article IV Consultation Staff Report.” Washington, DC.
International Monetary Fund (IMF). Forthcoming. “Regulating,
Supervising, and Handling Distress in Public Banks.” IMF
Departmental Paper, Washington, DC.
Jobst, Andreas A., and Hiroko Oura. 2019. “Sovereign Risk in
Macroprudential Solvency Stress Testing.” IMF Working Paper
19/266, International Monetary Fund, Washington, DC.
Laeven, Luc, and Fabian Valencia. 2018. “Systemic Banking
Crises Revisited.” IMF Working Paper 18/2076, International
Monetary Fund, Washington, DC.
Liu, Yan, José Garrido, and Chanda DeLong. 2020. “Private
Debt Resolution Measures in the Wake of the Pandemic.” IMF
Special Series on COVID-19, International Monetary Fund,
Washington, DC.
Ongena, Steven, Alexander Popov, and Neeltje Van Horen.
2019. “The Invisible Hand of the Government: Moral Sua-
sion during the European Sovereign Debt Crisis.” American
Economic Journal: Macroeconomics 11 (4): 346–79.
Reinhart, Carmen M., and Kenneth S. Rogoff. 2009. This Time
Is Different: Eight Centuries of Financial Folly. Princeton, NJ:
Princeton University Press.
Rigobon, Roberto. 2003. “Identification through Heteroskedas-
ticity.” Review of Economics and Statistics 85 (4): 777–92.

https://www.imf.org/en/Publications/CR/Issues/2022/02/10/South-Africa-2021-Article-IV-Consultation-Press-Release-Staff-Report-and-Statement-by-the-513001

Introduction
Technological change has been reshaping banking
services for years, but groundbreaking innovation
and widespread adoption have accelerated this pro-
cess globally. Fintech—technological innovation in
financial activities—is increasingly disrupting core
financial services traditionally provided by banks
The authors of this chapter are Jose Abad, Parma Bains, Yingyuan
Chen, Torsten Ehlers, Antonio Garcia Pascual (chapter lead), Fabiana
Melo, Junghwan Mok, Nobuyasu Sugimoto, Tomohiro Tsuruga,
Zhichao Yuan, and Xingmi Zheng. The chapter was written under
the guidance of Tobias Adrian, Fabio Natalucci, and Ranjit Singh.
and has gained even more momentum during the
COVID-19 pandemic (Figure 3.1, panel 1). At the
frontier of technological advancement is decentralized
finance (DeFi). DeFi is crypto-market-based financial
intermediation in which all financial transactions are
performed on a computer network without a cen-
tral intermediary. DeFi has been growing rapidly, in
tandem with the expansion of the crypto ecosystem
(Figure 3.1, panel 2).
Fintech firms herald efficiency gains, progress in
financial inclusion, and better customer experience
(IMF 2018). Fintech firms (hereafter referred to as
THE RAPID GROWTH OF FINTECH: VULNERABILITIES AND
CHALLENGES FOR FINANCIAL STABILIT Y3CHAPTE
R
65International Monetary Fund | April 2022
Chapter 3 at a Glance
• Fintech—technological innovation in financial activities—can reduce costs and frictions, increase efficiency
and competition, and broaden access to financial services.
• This chapter focuses on vulnerabilities and financial stability implications of the rapid growth of fintech firms
(“fintechs”), accelerated by the COVID-19 pandemic. Their fast growth into risky business segments, combined
with sometimes inadequate regulation and/or supervision, gives rise to systemic risks and potential financial
stability implications.
• Digital banks (“neobanks”) are growing in systemic importance in their local markets. A case study
on neobanks unveils several vulnerabilities: (1) higher risk-taking in retail loan originations without
appropriate provisioning and underpricing of credit risk; (2) higher risk-taking in the securities portfolio;
and (3) an inadequate liquidity management framework.
• Fintech firms not only take on risks themselves but also exert pressure on incumbents. The case study of
the US mortgage market presents evidence of a significant negative impact of competitive pressure from
fintechs on the income of traditional banks.
• By taking innovation to a new level, a form of financial intermediation based on crypto assets, known as
decentralized finance (DeFi), has had extraordinary growth in the past two years, potentially offering higher
efficiency and investment opportunities. DeFi is increasingly interconnected with traditional financial
intermediaries. While its market size is still relatively small, unregulated DeFi poses market, liquidity, and
cyber risks, against a backdrop of legal uncertainties.
• Policies that target both fintech firms and incumbents proportionately are needed. For neobanks, more robust
capital, liquidity, and operational risk-management requirements (at the entity and group levels) commensurate
with their risks are desirable. For incumbents, prudential supervision may need greater focus on the health of
less technologically advanced banks, as their existing business models may be less sustainable over the long term.
• The absence of centralized entities governing DeFi is a challenge for effective regulation and supervision.
Regulation should focus on elements of the crypto ecosystem that enable DeFi, such as stablecoin issuers and
centralized exchanges. Authorities should also encourage DeFi platforms to be subject to robust governance
schemes, including industry codes and self-regulatory organizations. These entities could provide an effective
conduit for regulatory oversight.

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G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
International Monetary Fund | April 2022
fintechs) hold the promise of reducing costs and
frictions related to informational asymmetry, increasing
efficiency and competition, and broadening access to
financial services, especially in low-income countries
and for underserved populations. Users of fintech
financial services more generally benefit from a better
experience through online access to financial services
on any device at any time. Taking financial innova-
tion a step further, DeFi has experienced substantial
growth in the past two years and has the potential to
offer even more innovative, inclusive, and transpar-
ent financial services thanks to greater efficiency and
accessibility.
The speed, reach, and depth of these changes give
rise to systemic risks and pose challenges to financial
stability. Fintechs are quickly making inroads into a
wide range of critical financial services—sometimes
aided by favorable regulatory treatment for spe-
cialized financial services. While some individual
fintechs are still small, they have the ability to scale
up very rapidly—often across both riskier busi-
ness segments and riskier clients than traditional
lenders. The combination of fast growth and the
increasing importance of fintech financial services
for the functioning of financial intermediation
gives rise to systemic risks. The speed and depth of
such changes further pose challenges for traditional
intermediaries.
In addition, DeFi often involves the buildup of
leverage, and is particularly vulnerable to market,
liquidity, and cyber risks as discussed in this chapter.
DeFi activities are so far taking place mainly in crypto
asset markets, but they can increase the interconnect-
edness of crypto investors. With the rapidly increasing
adoption of DeFi by institutional investors, the link-
ages with traditional financial institutions are growing.
DeFi may also accelerate the ongoing trend toward
cryptoization in some economies (see Chapter 2
of the October 2021 Global Financial Stability
Report [GFSR]).
Traditional bank
Fintech bank
Traditional nonbank
Fintech nonbank
Stablecoins (others, left scale)
Stablecoins (USDC, left scale)
Stablecoins (USDT, left scale)
Stablecoins total (left scale)
DeFi total (right scale)
Figure 3.1. The Rise of Fintech Firms and Decentralized Finance
The growth of fintechs has accelerated in recent years …
1. Growth of Assets of Fintech Lenders
(2013:H1=100)
80
100
120
140
160
180
200
220
240
260
280
300
320
14 15 16 17 18 19 20 212013
… as has the rise of assets in decentralized finance, driving growth in
stablecoins.
2. Total Nominal Value of Assets in Decentralized Finance and
Stablecoins
(Billions of US dollars)
0
20
40
60
80
100
120
0
20
40
60
80
100
120
140
160
180
Ja
n.
2
02
0
Fe
b.
2
0
M
ar
. 2
0
Ap
r.
2
0
M
ay
2
0
Ju
ne
2
0
Ju
ly
2
0
Au
g.
2
0
Se
p.
2
0
O
ct
. 2
0
N
ov
. 2
0
D
ec
. 2
0
Ja
n.
2
1
Fe
b.
2
1
M
ar
. 2
1
Ap
r.
2
1
M
ay
2
1
Ju
ne
2
1
Ju
ly
2
1
Au
g.
2
1
Se
p.
2
1
O
ct
. 2
1
N
ov
. 2
1
D
ec
. 2
1
Sources: CoinGecko; DeFi Pulse; S&P Global Market Intelligence; and IMF staff calculations.
Note: In panel 1, the sample comprises 13 advanced economies and 7 emerging market economies. In panel 2, total nominal value of decentralized finance (DeFi) is
the total value of all DeFi projects—all deposits and governance tokens held in a given platform on Ethereum blockchain as reported by DeFi Pulse. A stablecoin is a
type of crypto asset that aims to maintain a stable value relative to a specified asset or a pool of assets. USDC = USD Coin; USDT = Tether.

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International Monetary Fund | April 2022
As financial services move from regulated banks
to less regulated—or even unregulated—entities and
platforms, as in the case of DeFi, so do the associated
risks. This poses challenges for financial authorities in
the form of regulatory arbitrage, interconnectedness,
and contagion that require supervisory and regula-
tory action, including better consumer and inves-
tor protection.
This chapter takes a deep dive into the vulner-
abilities and financial stability implications of the
rapid growth of fintech. It focuses on fintechs and
fintech platforms (DeFi) that provide core banking
services: deposit-taking and credit intermediation.
While fintechs have made inroads into a broad range
of financial services, deposit-taking and credit inter-
mediation are central to both the functioning of an
economy and to financial stability.1 The chapter first
lays out a conceptual framework for the different types
of services provided by fintechs. It then presents two
case studies of fintechs in competition with traditional
banks: (1) digital banks (referred to as “neobanks”) in
both advanced and emerging economies; and (2) the
US mortgage origination market. The second half of
the chapter focuses on lending services in the novel
DeFi ecosystem, with a focus on its opportunities
and risks. The chapter concludes with some policy
recommendations.
Fintechs in Banking: Conceptual
Framework and Risks
The core business model of banks is both to collect
deposits and extend credit. In doing so, they fulfill the
key economic function of financial intermediaries: the
transformation of deposits (savings) into credit (invest-
ments), which entails liquidity, maturity, and credit
risk transformation.
Fintechs insert themselves at various points along
the financial intermediation chain, usually by pro-
viding specialized services (Figure 3.2). In doing so,
fintechs can quickly develop innovative solutions that
can offer efficiency gains or better customer experience.
1Fintechs have made inroads into many other financial services,
including payments, asset management, insurance, and crypto
assets (Drakopoulos, Natalucci, and Papageorgiou 2021), which are
beyond the scope of this chapter. Regarding data privacy concerns
raised by technological developments in finance and the rise of large
technological firms (big techs), the reader is referred to Haksar and
others (2021).
The increased competition traditional banks face from
fintechs is generally beneficial from an economic point
of view. Some fintechs might fall outside traditional
banking regulations, as most jurisdictions allow for
more lenient regulatory requirements, or can even be
unregulated to some extent, as in the case of DeFi. The
way in which fintechs insert themselves in the financial
intermediation chain therefore has different implica-
tions for financial stability risks:
• The most common approach consists of banks
cooperating with fintechs by using their services
or through mergers and acquisitions. Although
banks have been increasing IT-related expendi-
tures,2 using or acquiring the services of fintechs
can be an effective means of technology adop-
tion. Likewise, fintechs have been acquiring and
using the services of banks. However, the use of
third-party services presents challenges if they are
an integral part of risk management, compliance,
or fulfillment of regulatory requirements, such as
“know your customer” or anti–money laundering/
combating the financing of terrorism (AML/CFT).
If a large number of banks rely on the same service
providers, outages or cyber incidents could give rise
to systemic risks.
• A more notable form of disruption arises from
direct competition for the same services. Direct
competition is more likely in jurisdictions where
banks are less prevalent and in consumer-facing
services (Boot and others 2021). In core banking
services, some of the largest fintechs have grown
very quickly in emerging markets—for example,
Mercado Libre in Latin America, which offers a
range of services, including credit to small and
medium enterprises (SMEs). Direct competition
in customer-facing services is lucrative for fin-
techs, thanks to typically higher margins than for
business-to-business services.
• When fintechs provide bank-like services but oper-
ate under less stringent regulations than banks,
financial stability risks can arise. The business
model of fintechs relies on rapid growth, which—
in the absence of appropriate regulations—can
lead to excessive risk-taking, including by banks
2The largest US global bank is planning to invest $12 billion
to develop technological solutions (“JPMorgan plots ‘astonishing’
$12bn tech spend to beat fintechs” [Financial Times,
January 15, 2022]).

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International Monetary Fund | April 2022
trying to defend their market position (see the
case study on the US mortgage market). This can
lead to capital erosion and higher systemic risk
(Vives 2019).
• An important, special case of direct competition with
banks is that of digital banks. They are often—but
not always—fully licensed banks that compete with
traditional banks across a broad range of core bank-
ing services and tend to follow a technology-driven
business model with some inherent risks, as docu-
mented in the next section’s case study.
• In the most radical and disruptive approach fintechs
shortcut the intermediation chain to remove the
financial intermediary altogether. Peer-to-peer lend-
ing platforms, for instance, directly connect savers
and investors with borrowers. In this case, investors
commit their funds for a given time horizon and
effectively assume credit and liquidity risks. In DeFi,
liquidity providers—depositors—are exposed to
DeFi platforms’ run risk, while borrowers provide
large amounts of collateral to eliminate credit risks
(see the DeFi section later in this chapter).
Case Study: Neobanks
Digital banks, or neobanks, are direct—branchless—
banks that acquire and serve customers primarily
through digital touchpoints such as mobile apps.3
3This case study is based on 37 neobanks and 640 traditional
banks in 18 economies. Neobanks, which have a higher-than-average
risk profile (Figure 3.4), are compared against the asset-weighted
average of the universe of traditional banks in their respective local
markets (a measure of average bank risk). With the exception of one
neobank regulated as a payment company, all other neobanks in our
sample have banking licenses. Online Annex 3.1 describes both the
data and methodology.
Fintechs insert themselves into the financial intermediation chain or circumvent it in the case of DeFi.
Source: IMF staff.
Note: AMF/CLT = anti-money laundering/combating the financing of terrorism; BaaS = Banking as a Service; DeFi = decentralized finance; KYC = Know Your
Customer; P2P = peer to peer; SME = small and medium enterprise.
Figure 3.2. Fintechs in the Core Banking Intermediation Chain
DeFi, P2P lending
Bo
rr
ow
er
s
Sa
ve
rs
/d
ep
os
ito
rs
/
liq
ui
di
ty
p
ro
vi
de
rs
Neobanks
Part of regular intermediation chain and ability to conduct core transformation functions
bypassing/shortcutting
intermediation chain
bypassing/shortcutting
intermediation chain
Services and institutions in blue are analyzed in this chapter
Traditional banks
Assets Liabilities
Credit Deposits
Liquidity, maturity, and credit risk transformation
via deep balance sheet
Insertion into
intermediation
chain
Credit provision
(longer-term and risky)
Insertion into
intermediation
chain
Deposit-taking
(liquid, short-term, and safe)
SME lending, supply
chain financing, trade
credit, mortgage
origination, automobile
financing, consumer
credit (including buy
now pay later), credit
cards, etc.
Fintechs competing in
credit provision
Deposit-taking (where
allowed), savings
products and
“out-of-wallet tools”
(including based on
open banking), personal
finances, P2P payment
tools, etc.
Fintechs competing in
deposit-taking
Credit intermediation
processes:
Loan application processing,
screening, underwriting,
credit scoring, loan servicing
and collections, BaaS
solutions, risk management,
customer management, etc.
Deposit-taking
processes:
Customer onboarding,
identity verification,
regulatory process solutions
(KYC and AML/CFT checks,
regulatory reporting), BaaS
solutions, etc.
Fintechs providing services to banks

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C H A P T E R 3 T h E R A P I d G R O w T h O F F I N T E C h : v U L N E R A B I L I T I E S A N d C h A L L E N G E S F O R F I N A N C I A L S T A B I L I T Y
International Monetary Fund | April 2022
They aim to distinguish themselves from traditional
banks through digital technologies, such as cloud com-
puting, application programming interfaces, big data,
and artificial intelligence, making banking services
available on any device at any time. Neobanks tend to
target financially underserved clients.
Neobanks are growing in systemic importance in
their respective local markets. They have reached mar-
ket capitalization nearly as large as that of some of the
largest traditional banks (Figure 3.3, panel 1). Despite
their currently relatively modest balance sheet size,
the high valuations of some neobanks are driven by
expectations for strong loan growth, particularly in the
unsecured retail segment (Figure 3.3, panel 2).
Rapid scaling may be a source of value, but it may
also carry higher operational risks. Rapid scaling is a
key feature of neobanks, and of young firms more gen-
erally, as future growth is their main source of value.
Rapid growth may also translate into the buildup of
operational risks. Furthermore, evidence points to
higher and increasing fraud through digital channels
(UK Finance 2021), suggesting that neobank clients
may be more vulnerable to fraud than traditional
bank clients.
Credit Risk: High, Underprovisioned, and Underpriced
Neobanks target borrowers with a riskier credit
profile. Neobanks tend to explicitly address financially
underserved clients across the consumer/credit card
and SME segments in the context of heavily skewed/
concentrated—less diversified—loan portfolios. In
practice, this means serving younger individuals4 with
lower incomes (Figure 3.4, panel 1) and lower credit
scores by granting them loans that are mostly unse-
cured (Figure 3.4, panel 2) or concentrated around
risky sectors, such as commercial real estate (for exam-
ple, SME loans by UK neobanks).
4While neobanks’ exposure to relatively younger populations with
lower incomes and credit scores poses risks, it may not only represent
a higher appetite for risk but could also reflect higher technological
literacy in this demographic group.
Leading neobank
Leading traditional bank
Unsecured retail
Jeonse
Housing
SoHo
Total
Figure 3.3. The Increasing Relevance of Neobanks
Some neobanks are among the largest players in their local markets
and have large valuations …
1. Valuation of Selected Leading Neobanks
(Billions of US dollars, as of late March 2022; for Russia: data as of
January 2022)
0
5
10
15
20
25
30
35
40
45
50
55
Brazil UK KazakhstanKorea Russia Germany
… driven by expectations for strong loan growth, particularly in the
unsecured segment.
2. Korean Digital Banks: Loan Market Share
(Percent of loans outstanding, 2021–25, expected)
2020 2021E 2022E 2023E 2024E 2025E
0
2
4
6
8
10
12
Sources: Bloomberg Finance L.P.; Morgan Stanley Research; S&P Global Market Intelligence; and IMF staff calculations.
Note: Panel 1 shows the largest neobanks based on market capitalization or private valuation data. The leading traditional banks are the largest domestic banks
according to assets (the second largest for Germany, Russia, and the United Kingdom). The sample of neobanks used in the case study includes the six shown above,
with the exception of the UK one, for which the focus is just on its retail banking subsidiary that operates outside the UK and is significantly smaller in size (as the
parent company is an e-money provider without a full banking license). In panel 2, SoHo refers to small professional businesses; Jeonse refers to special housing
lease contracts in Korea. E = expected.

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G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E
International Monetary Fund | April 2022
Despite greater credit exposure, neobanks’ over-
all credit risk coverage level remains significantly
below that of traditional banks. Higher credit risk
(Figure 3.5, panel 1) should translate into a higher
expected loss and, in turn, into higher coverage ratios.
However, neobanks’ loan loss reserves as a propor-
tion of their overall (risk-weighted) assets are well
below those of traditional banks (Figure 3.5, panel 2),
implying relatively looser provisioning standards or
practices.5
Neobanks also seem to be underpricing credit risk.
Neobanks feature asset yields that are typically higher
than those of banks. This seems to be driven by higher
yield on their securities portfolio rather than yields
on their loan book, as the latter are broadly equal to
those of banks. A meaningfully negative risk-adjusted
net interest margin points to underpricing of credit
risk in their lending business in parts of our sample
as well as in some regions (Figure 3.6, panel 1). This
could be due to competition vis-à-vis traditional
banks and/or other neobanks. Importantly, their
5Neobanks also seem to operate with higher leverage (total equity/
assets) ratios relative to traditional banks. This, however, seems
related to the fact that they are young companies in their growth
phase that are still loss-making for the most part (Figure 3.6, panel
3); hence they initially need higher equity. For mature neobanks, the
capital advantage disappears.
risk-adjusted loan margins would be even lower if
their cost of risk adequately reflected their more pre-
carious credit-risk profile and their lower loan-related
fee income were also accounted for (more on this
later in the chapter). Ultimately, higher asset yields
and overall net interest margins reflect an implicit
cross-subsidy through neobanks’ high-yielding (riskier)
securities portfolios.
Liquidity Risks: Lower Liquidity Coverage Adds Risk
Lower liquidity coverage may pose additional risks.
On the one hand, neobanks’ client base is younger
(Figure 3.4, panel 1) and likely to be less loyal, imply-
ing that their deposits could be less sticky. Therefore,
caution would call for neobanks to operate with higher
liquidity coverage ratios, in line with Basel III require-
ments.6 Instead, their ratio of liquid assets to total
deposits—a measure of liquidity risk—is lower than
that of banks (Figure 3.6, panel 4). On the other hand,
the composition of their liquid asset portfolios shows
that neobanks have a much larger share of interbank
6For the calibration of the liquidity coverage ratio under Basel III,
“less stable deposits” (including “internet deposits”) are assigned a
runoff rate of at least 10 percent (3 percent for “stable deposits”);
supervisors may assign higher rates.
>35 years old
<35 years old Lower income Middle and higher income Neobanks Traditional banks Figure 3.4. Client Profile of Neobanks Clients are younger and have lower incomes ... 1. Brazilian Banks: Customer Breakdown (Percent, share of loans) Income bracket 0 20 40 80 60 100 Neobank Traditional peers 2. UK Banks: Unsecured Exposures (Percent of total loans, 2020; bars depict individual banks) Age bracket 0 20 40 80 60 100 Neobank Traditional peers ... and there is more focus on unsecured lending. 0 100 10 20 30 40 50 60 70 80 90 Sources: Company filings; Nu Holdings; S&P Global Market Intelligence; and IMF staff calculations. 71 C H A P T E R 3 T h E R A P I d G R O w T h O F F I N T E C h : v U L N E R A B I L I T I E S A N d C h A L L E N G E S F O R F I N A N C I A L S T A B I L I T Y International Monetary Fund | April 2022 loans than traditional banks. This also suggests that neobanks are more interconnected than traditional banks with the rest of the banking system. Weak Retail Banking Returns Neobanks display higher operating expenses and lower potential for fee income generation. Some- what counterintuitively, neobanks appear to be less cost-efficient than traditional banks (Figure 3.6, panel 2).7 This is driven by persistently higher nonstaff expenses8 on the back of either higher 7Our results are similar for overall operating expenses as a propor- tion of either total income or business volumes. Mature neobanks (defined as those established before 2010) remain more inefficient, but the difference is lower. 8Staff expenses are defined as “compensation & benefits” expenses for all (neo)banks with data available in the S&P Global Market Intelligence database. Nonstaff expenses are defined as the difference between staff and total operating expenses. customer acquisition costs (such as marketing)9 and/or higher compliance-related costs (such as those related to anti–money laundering and cyber- security). In addition, the lower income profile of neobank customers limits the potential for cross-selling insurance, wealth management, and other fee-income-generating products.10 If securities income is excluded, neobanks’ margin advantage fades (Figure 3.6, panel 1). Overall, neobank returns appear weak (Figure 3.6, panel 3), with only a few neobanks generating profits. Overall, emerging market neobanks tend to fare better than advanced economy neobanks. Emerging market neobanks display relatively lower liquidity risk than advanced economy neobanks with a stronger 9These costs might constitute an initial investment needed to build up market share. 10Group-level consolidated data are used, with a few exceptions where only unconsolidated data were available. LLRs (% earning assets) LLRs (% RWAs) Figure 3.5. Credit Risk Profile Neobanks have high credit costs and a riskier client base ... 1. Neobanks: Cost of Risk (CoR) (Loan loss provisions/gross loans; in number of standard deviations vs. banks) –0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Al l Pr e- 20 10 Po st -2 01 0 Pr ofi ta bl e Lo ss -m ak in g Eu ro pe As ia W es te rn H em is ph er e AE s EM s ... but coverage falls short of traditional banks. 2. Neobanks: Coverage (Loan loss reserves; in number of standard deviations vs. banks) –1.0 –0.8 –0.6 –0.4 –0.2 0.0 0.2 0.4 0.6 0.8 1.0 Al l Pr e- 20 10 Po st -2 01 0 Pr ofi ta bl e Lo ss -m ak in g Eu ro pe As ia W es te rn H em is ph er e AE s EM s Sources: Company filings; S&P Global Market Intelligence; and IMF staff calculations. Note: The figure panels show neobanks’ distance (median number of standard deviations) from (the asset-weighted average of) traditional banks (see details in Online Annex 3.1). In panel 1, a positive (negative) number implies a higher (lower) cost of risk for neobanks compared with their respective traditional-bank peer group; the related exposures should be viewed as riskier (less risky). In panel 2, a positive (negative) number implies a higher (lower) coverage level at neobanks compared with their traditional-bank peer group, consistent with a higher (lower) expected loss. AEs = advanced economies; EMs = emerging markets; LLRs = loan loss reserves; RWAs = risk-weighted assets. 72 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E International Monetary Fund | April 2022 revenue profile and wider loan and fee margins. This seems to be related to life cycle factors (in light of the larger portion of “mature” neobanks in the emerg- ing market subsample), but also to business models (given the relatively strong performance of Chinese neobanks).11 11In China, neobanks and big tech overlap, with the three Chinese neobanks in our sample backed by major local big techs. Case Study: Fintechs in the US Home Mortgage Market Fintechs in the US home mortgage market have been active for more than a decade. Fintechs remove the need for physical branches in mortgage orig- ination. The main advantage of fintech mortgage originators is arguably the use of technology (Buchak and others 2018). This has afforded them efficiency gains, as they process applications about 20 percent Risk-adjusted NIM (NIM - CoR) Risk-adjusted NIM (NIM - CoR) - loans Net fee and commission income Cost/business volumes Non-staff cost/business volumes PBT profitability (% equity) PBT profitability (% equity) - loans 1. Neobanks: Net Interest Margin (NIM) (Percent of earning assets; in number of standard deviations vs. banks) 2. Neobanks: Operating Expenses (Percent of business volumes; in number of standard deviations vs. banks) 3. Neobanks: Pre-Tax Return on Equity (ROE) (Percent of total equity; in number of standard deviations vs. banks) 4. Neobanks: Liquid Assets over Deposits (Percent of deposits; in number of standard deviations vs. banks) ... and have underwhelming banking returns ... High net interest margins are driven by the securities portfolio. Neobanks tend to be less efficient ... ... as well as weaker liquidity ratios. Sources: Company filings; S&P Global Market Intelligence; and IMF staff calculations. Note: The figure panels show neobanks’ distance (median number of standard deviations) from traditional banks. In panel 1, a positive (negative) number implies a larger (lower) net interest margin relative to traditional banks. In panel 2, a positive (negative) number implies lower (higher) cost efficiency relative to traditional banks. In panel 3, a positive (negative) number implies a larger (lower/negative) return on equity than at traditional banks. In panel 4, a positive (negative) number implies a higher (lower) coverage than traditional banks. AEs = advanced economies; CoR = cost of risk; EMs = emerging markets; NIM = net interest margin; PBT = profit before tax. Figure 3.6. Margins, Profitability, and Liquidity Profiles of Neobanks –4 –2 0 2 4 0.0 4.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 –6 2 –4 –2 0 –5 –3 –1 1 –0.5 0.0 –0.4 –0.4 –0.3 –0.3 –0.2 –0.2 –0.1 –0.1 Al l Pr e- 20 10 Po st -2 01 0 Pr ofi ta bl e Lo ss -m ak in g Eu ro pe As ia W es te rn H em is ph er e AE s EM s Al l Pr e- 20 10 Po st -2 01 0 Pr ofi ta bl e Lo ss -m ak in g Eu ro pe As ia W es te rn H em is ph er e AE s EM s Al l Pr e- 20 10 Po st -2 01 0 Pr ofi ta bl e Lo ss -m ak in g Eu ro pe As ia W es te rn H em is ph er e AE s EM s Al l Pr e- 20 10 Po st -2 01 0 Pr ofi ta bl e Lo ss -m ak in g Eu ro pe As ia W es te rn H em is ph er e AE s EM s 73 C H A P T E R 3 T h E R A P I d G R O w T h O F F I N T E C h : v U L N E R A B I L I T I E S A N d C h A L L E N G E S F O R F I N A N C I A L S T A B I L I T Y International Monetary Fund | April 2022 faster than other lenders (Fuster and others 2019). A fintech firm has been the single largest originator for several years, even though banks have contin- ued to wield a substantial market share (Figure 3.7, panel 1).12 Fintechs pursue an aggressive growth strategy and serve younger and riskier borrowers. Their mortgage originations have tended to substantially outpace those of banks and other nonbanks in periods of overall market expansion (Figure 3.7, panel 2).13 Their ability to grow rapidly thanks to their technology and internet-based business model is highlighted by the rapid growth of recently established fintech mortgage firms. Fintech mort- gages, and particularly those originated by younger fintech firms, are more popular among relatively younger borrowers, who tend to have lower incomes (Figure 3.7, panel 3). Fintechs also originated riskier mortgages with higher loan-to-value ratios during 2018–20 (Figure 3.7, panel 4). At the same time, fintechs improve access to mortgages in less affluent neighborhoods (see Online Annex 3.2, which also provides a data description and details on the empirical analyses).14 Fintechs directly compete with banks, raising financial stability challenges. Fintechs are present in all locations, including those with a higher density of bank branches (Figure 3.7, panel 5, and Online Annex 3.2). Critically, competitive pressure from fintechs—measured as the (previous period) increase in fintech market share (by mortgage origination amount) in ZIP code areas where a given bank is active— appears to have had a significant effect on banks’ interest income from mortgages (Figure 3.7, panel 6). A 1 percentage point rise in the composite market share of fintechs is associated with a 0.4 percentage point decline in (gross) mortgage interest income— this is more than 2.5 percentage points of the sample median of 16.8 percent. Importantly, expenditures by banks related to data processing (operation or pur- chase of IT services and software) can offset the loss of 12The analysis uses Home Mortgage Disclosure Act data from 2007–20, covering more than 100 million US mortgage originations (see Online Annex 3.2). 13Nonbanks are financial institutions that do not take deposits. All fintechs are nonbanks. 14Jagtiani, Lambie-Hanson, and Lambie-Hanson (2021) find that fintechs have high market shares in areas with low credit scores and high mortgage denial rates. mortgage-related income.15 This points to the impor- tance of technology adoption for traditional banks— either through organic solutions or third-party services (these results are robust across alternative specifications; see Online Annex 3.2). Banks have not faced full-scale disintermediation despite intense competition from fintechs. The share of mortgage assets does not seem to have been significantly affected during 2007–20. This can also be attributed to the limited role of fintechs as originators, whereas banks retained about 40 percent of the mortgages they originated on their balance sheets (Online Annex 3.2). Banks also continue to attract deposits, since fintechs in the mortgage-origination market are not deposit-taking institutions. Decentralized Finance: Vulnerable Efficiency Decentralized finance (DeFi) refers to financial applications—called “smart contracts”—processed by computer code on blockchains, with limited or no involvement of centralized intermediaries. Key features of DeFi are automated and decentralized record keeping, risk-taking, and decision-making within the crypto ecosystem (Table 3.1). Operations within DeFi are automated via smart contracts, and all contractual and transaction details are recorded on the network. Decisions such as changes in collateral requirements or distribution of profits are made by users with voting rights, which often accompany use of the platform. Consequently, DeFi offers broad access to players of any size and has no need for custodian service, potentially improving efficiency and financial inclusion. Three key technological advances have contributed to the expansion of DeFi. First, the launch of block- chain technology provided a digital infrastructure to record value on a distributed system open to everyone, and in which transaction records of crypto assets are validated without the need for a single trusted entity. Blockchain is a type of distributed ledger technology.16 15The regression results shown imply that banks with IT expendi- tures higher by about 3.7 percent of bank equity can fully make up for the loss of income from a 1 percentage point increase in the fin- tech composite market share. There is, however, no evidence that IT expenditures can reduce the marginal effect of competition itself—it can only offset the effect on income. 16Distributed ledger technology enables a single, sequenced, stan- dardized, and cryptographically secured record of activity to be safely distributed to, and acted on by, a network of varied participants. See Garrido and others (2022). 74 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : S h O C k w A v E S F R O M T h E w A R I N U k R A I N E T E S T T h E F I N A N C I A L S Y S T E M ’ S R E S I L I E N C E International Monetary Fund | April 2022 Banks Nonbanks - total Nonbanks - non-fintechs Fintechs Credit unions Rank of RM (right scale) Total originations Banks Nonbanks - non-fintechs Fintechs New fintechs (right scale) Statistically significant Not significant Fintech - refinancingFintechs - home purchases Banks - home purchases Banks - refinancing Banks Fintechs New fintechs Nonbanks - non-fintechs Median income (right scale) 3. Age Distribution of Mortgage Borrowers (Percent, left scale; mn USD, right scale) 4. Distribution of Loan-to-Value Ratios, 2018–20 (Smoothed cumulative distribution) 5. Fintech Origination vs. Density of Bank Branches (Percent) 6. Effect of Competitive Pressure from Fintechs on Banks (Percentage points) Fintech mortgage origination is only marginally lower in areas with high bank penetration. Competitive pressure from fintechs has had a significant effect on banks’ mortgage income. Fintechs are more prevalent among younger and lower-income borrowers. Fintechs have tended to originate riskier mortgages. Sources: Federal Deposit Insurance Corporation; National Bureau of Economic Research ZIP Code Distance Database; US call reports; US Census Bureau; US Home Mortgage Disclosure Act; and IMF staff calculations. Note: In panel 1, RM is Rocket Mortgage. Originations include both refinancing and new purchases of one- to four-family homes. Definitions of variables and model specifications for panel 6 are provided in Online Annex 3.2. IT = information technology. Figure 3.7. Fintechs in the US Home Mortgage Market Loan-to-value ratio (percent) 1. Annual US Home Mortgage Originations (Trillions of US dollars, left scale; rank, right scale) 2. Growth in US Home Mortgage Originations (Percent per year) Fintechs and other nonbanks had a long-standing presence in the mortgage market. Originations by fintechs have been growing faster than banks, particularly during periods of high growth. 0 2 1 3 1 5 9 13 17 21 2007 08 09 10 11 12 13 14 15 16 17 18 19 20 2007 08 09 10 11 12 13 14 15 16 17 18 19 20 0 100 200 300 400 500 –30 0 30 60 90 120 150 Lower share of riskier mortgages 0 10 20 30 40 50 0.0 0.2 0.4 0.6 0.8 1.0 0 20 40 60 80 100<25 25–34 35–44 45–54 55–64 65–74 >74
Age groups
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.11
0
10
20
30
40
50
60
Li
ke
lih
oo
d
of
F
in
te
ch
m
or
tg
ag
e
or
ig
in
at
io
ns
0 20 40 60 80 100
Fintech
competitive
pressure on
mortgage
income
IT expenditure
on mortgage
income
Fintech
competitive
pressure on
deposit
financing share
Fintech
competitive
pressure on
mortgage
lending share
Percent of bank equity Percent change
–0.8
–0.6
–0.4
–0.2
0.0
0.2
Number of bank branches within 10-mile radius of borrower

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C H A P T E R 3 T h E R A P I d G R O w T h O F F I N T E C h : v U L N E R A B I L I T I E S A N d C h A L L E N G E S F O R F I N A N C I A L S T A B I L I T Y
International Monetary Fund | April 2022
Second, the invention of the smart contract made
it possible for blockchain technology to change the
manner of financial intermediation. A smart con-
tract is computer code that allows for transactions to
be executed when certain predetermined conditions
are met. DeFi is the application of smart contracts
for financial intermediation such as deposit-taking,
lending, derivative trading, and the exchange of crypto
assets. Third, offerings of stablecoins pegged to existing
sovereign currencies were a key innovation. Stablecoins
are used in DeFi as a unit of account, medium of
exchange, and store of value. The growth of stable-
coins and evolution of DeFi have evolved in tandem
(Figure 3.1, panel 2).
DeFi has the potential to offer financial services
with even greater efficiency, becoming a gravita-
tional force that attracts a large number of crypto
investors. However, it may also come at the cost
of greater risks and uncertainties. This section will
analyze some of the key risks and opportunities of
DeFi lending and discuss how authorities should
prepare for it.
A Primer on DeFi Lending
DeFi has expanded rapidly, offering blockchain-based
financial services in the crypto ecosystem. Among many
services, the debt outstanding of DeFi lending has
increased markedly since 2020, supported by the wider
use of stablecoins (Figure 3.8, panel 1). DeFi provides
crypto asset holders the opportunity to earn interest by
depositing crypto and/or borrowing more crypto by
posting collateral.
DeFi lending platforms receive crypto assets as
deposits and lend them out to borrowers who meet
certain collateral criteria. A DeFi lending service
works as follows:
• Deposits: Users can earn interest by depositing their
crypto asset in a “liquidity pool” specific to each type
of crypto asset. Users with deposits in the same assets
receive the same interest rate. In exchange, the deposi-
tor receives a platform-specific utility token that works
as a certificate of deposit17 (Figure 3.8, panel 2, step 1).
The token has a value equivalent to the underlying asset
deposited but bears interest. A depositor can withdraw
the deposit at any time (Figure 3.8, panel 2, step 2).
• Borrowing: A user with deposits (that is, a user
who owns the utility token) can borrow a crypto
asset from a liquidity pool by posting the deposited
asset as collateral (Figure 3.8, panel 2, step 3). The
lending interest rate varies, depending on the level
of utilization for the borrowing asset.18
• Collateral: Collateralization is the key to safeguard-
ing the platform from market risks associated with
lending. Lending platforms often require overcol-
lateralization by setting a discount factor (called a
collateral factor) typically ranging from 0 to 0.8
across different types of assets. For example, when
the collateral factor is 0.8, borrowers can borrow up
to 80 percent of the collateral value posted; when
a collateral factor is zero, however, as in the case of
Tether (USDT) in some DeFi platforms, the user
cannot borrow using the asset as collateral.
17For example, if a user deposits Ethereum (underlying asset) in
a DeFi platform, such as Aave or Compound, the user will receive
aETH and cETH (tokens), respectively.
18The utilization rate of a crypto asset is the ratio of the total
amount of loans to the total deposits of that asset in the platform.
The lending rate is lower when the platform has more available
liquidity in the deposit pool.
Table 3.1. Comparison of Decentralized Finance and Traditional Financial Services
Decentralized Finance Traditional Financial Services
Access World Wide Web
Permissionless and anonymized
Branch office
Compulsory know your customer/anti–money
laundering
Operation Automated by smart contract Mostly manual
Instruments Crypto assets, including stablecoins Fiat-currency-denominated financial assets
Record keeping Distributed ledger (verified by multiple network
participants)
Centralized ledger (verified by a single trustworthy
entity that operates the platform)
Decision-making Voting by users who own governing stakes Governed by top management (such as the bank
executive board)
Risk-taking Distributed to users Concentrated in a single trustworthy entity
Source: IMF staff.

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International Monetary Fund | April 2022
• Repayment and liquidation: Borrowers can repay
the debt at any time (Figure 3.8, panel 2, step 4).
However, borrowers must meet the collateral
requirements at all times. If at any time a borrow-
er’s collateral requirement falls below the required
threshold as a result of adverse price movements,
liquidation can be triggered by a liquidator who
repays the debt and acquires the collateral in
exchange for rewards—the liquidation bonus
(Figure 3.8, panel 3).
Leveraged longs and short selling are frequent strat-
egies employed by DeFi users. The DeFi lending
platform offers services that allow investors with crypto
assets to borrow other crypto assets. Investors may
form a leveraged long position (borrow stablecoins to
buy risky crypto assets) or form a short sell position
(borrow risky crypto assets and buy back later). The
most typical position is to borrow stablecoins against
volatile collateral. More than 90 percent of DeFi lend-
ing is denominated in stablecoins, while 75 percent of
the collateral is denominated in volatile crypto assets
(Figure 3.8, panel 4). As of the end of 2021, volatile
crypto assets such as Ethereum and Wrapped Bitcoin
were the dominant collateral. These use cases are often
seen in activities such as trading and market mak-
ing, which bring about higher market liquidity and
efficiency, but also help build leverage and destabilize
Borrower
DeFi
platform
(3) Borrow
crypto asset
(1) Borrow
Crypto assets
Crypto assets
Crypto assets
(4) Repay
(2) Withdraw
(1) Deposit
crypto asset
Depositor
Borrower
DeFi
platform
Liquidator
Utility token
(certificate)
Utility token (certificate)
Utility token
(certificate)
Utility token
(certificate)
Stablecoins 90%
Volatile assets 75%
(2) Liquidate
(collateral + liquidation bonus)
(3) Repay
(principal + interest)
DAI USDC WETH USDT
WBTC Others Total
Stablecoins Volatile assets
1. Total Debt Outstanding of DeFi Lending
(By type of crypto asset, billions of US dollars)
2. The Flow of a DeFi Lending Transaction
3. Liquidation 4. Composition of Borrowing and Collateral
(Percent)
If a borrower fails to maintain the required level of collateral, the
position is liquidated.
The volume of DeFi lending has increased rapidly, supported by wider
use of stablecoins.
DeFi lending platforms receive crypto assets as deposits and provide
collateralized loans.
Most lending is against stablecoins backed by volatile crypto assets.
Figure 3.8. Recent Development of DeFi Lending
0
10
5
15
20
25
30
2019:
Q1
19:
Q2
19:
Q3
19:
Q4
20:
Q1
20:
Q2
20:
Q3
20:
Q4
21:
Q1
21:
Q2
21:
Q3
21:
Q4
0 20 40 60 80 100
Sources: Aave v2; Compound v2; C.R.E.A.M. Finance; DeFi Llama; DeFi Pulse; The Graph; and IMF staff calculations.
Note: In panel 1: DAI, USDC, USDT, WETH, and WBTC represent DAI, USD Coin, Tether, Wrapped Ethereum, and Wrapped Bitcoin, respectively. DeFi = decentralized
finance.
Collateral
Borrowing

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C H A P T E R 3 T h E R A P I d G R O w T h O F F I N T E C h : v U L N E R A B I L I T I E S A N d C h A L L E N G E S F O R F I N A N C I A L S T A B I L I T Y
International Monetary Fund | April 2022
the market if used for speculation. Considering its
potential and the ongoing trend toward cryptoization
in some economies (see Chapter 2 of the October
2021 GFSR), DeFi lending could soon be expanded to
broader financial activities, such as mortgage lending,19
consumer finance, and so on.
Similar to traditional lending, DeFi is not free
from market, liquidity, credit, operational, and cyber
risks. DeFi lending can incur losses under unfavorable
market conditions, and liquidity mismatches can be a
cause for failure to meet redemption requests. More-
over, it appears to be more vulnerable to cyber and
AML/CFT risks, due to loopholes in computer code
and the anonymity of the platform.
Market Risks: Vulnerable to Crypto Market Volatility
Volatile crypto asset prices lead to frequent liqui-
dation of DeFi loans (Figure 3.9, panel 1). Liquida-
tion is triggered when a borrower fails to maintain
the collateral requirement or when the borrower’s
19MakerDAO, one of the largest DeFi platforms, has already
started offering mortgage loans against existing real estate.
loan-to-value ratio breaches a certain threshold. The
loan-to-value ratio is marked to market and can
swing considerably during volatile market condi-
tions. Large liquidations have occurred during sharp
declines in crypto asset prices. During the January
2022 crypto sell-off, liquidation across platforms
surged to the highest level since May 2021, erasing
$50 billion in asset value borrowed (Figure 3.9,
panel 1). When the collateral shortfall is large during
periods of high market volatility, liquidation can be
costly. Without timely liquidation, the shortfall will
be left unaddressed and could potentially undermine
platform solvency.20,21
Indeed, the asset quality of DeFi lending varies
considerably across assets and borrower risk profiles.
20Another source of liquidation risk comes from the precision
of the information source used in the platform to value its loans
and collateral. If the platform is misinformed about the asset
prices used in loans and collateral, it may trigger a cascade of
liquidations.
21The deterioration of the loan quality of the platform may
not materialize as a credit loss. This is because the loan has no
maturity, and there are no accounting rules for provisioning or
recognition of fair value loss. However, it can potentially reduce
the interest.
Probability of liquidation Expected loss from liquidation
All
Low-leveraged borrower
High-leveraged borrower
Total liquidation
BTC price (right scale)
1. Liquidation Volume and Bitcoin Price
(Millions of US dollars; US dollar per bitcoin)
2. Liquidation Probability and Expected Losses
(Percent)
High volatility of crypto asset prices leads to frequent liquidation of
DeFi lending.
Lending to riskier borrowers tends to be liquidated more often with
larger losses.
Sources: Aave v2; Bloomberg Finance L.P.; CoinGecko; Compound v2; C.R.E.A.M. Finance; The Graph; and IMF staff calculations.
Note: For panel 2, see Online Annex 3.3 for details on the probability and expected loss calculation. BTC = Bitcoin; DeFi = decentralized finance.
0
350
50
100
150
200
250
300
0
35
5
10
15
20
25
30
0
80,000
10,000
20,000
30,000
40,000
50,000
60,000
70,000
Figure 3.9. Decentralized Finance Market Risks
Jan. 2020 May 20 Sep. 20 Jan. 21 May 21 Sep. 21 Jan. 22

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International Monetary Fund | April 2022
Similar to the concept of default probability in
traditional loans, the probability of liquidation is esti-
mated in this section through a stochastic model. Liqui-
dation is triggered when the total value of borrowing
exceeds the threshold, defined as total collateral value
discounted by collateral factors (see Online Annex 3.3
for details). The modeled probability of liquidation
reflects the trend and volatility of the underlying crypto
assets, as well as the initial balance of debt outstand-
ing (the leverage). The expected loss reflects mainly the
loss of collateral value upon liquidation. The results
indicate that the one-year probability of liquidation is
24 percent on average, reflecting high volatility and a
rising trend in crypto prices (Figure 3.9, panel 2). In
particular, riskier (highly leveraged) borrowers tend to
exhibit higher liquidation probability. The expected loss
is largely mitigated by overcollateralization, but still
averaged about 0.9 percent, with larger losses incurred
by riskier borrowers.22
22Even though DeFi lending is overcollateralized, the value of
borrowing and repayment depends on the remaining balance of
collateral relative to the debt outstanding at the time of liquidation.
If the value of the borrowed token and/or collateral change abruptly,
timely liquidation will fail, resulting in liquidation losses.
Liquidity Risks: Heavily Concentrated
Liquidity could become insufficient during periods
of market stress. Depositors provide liquidity to DeFi
lending platforms, which facilitates lending these
deposits to borrowers. The total amount of loans that
can be issued is capped by the total amount of depos-
ited assets, or liquidity, on each platform. Similar to
the loan-to-deposit ratio in traditional banking, the
utilization rate measures how much of the liquidity
for a particular crypto asset has been loaned out on
each DeFi platform (Figure 3.10, panel 1).23 When
demand for borrowing a crypto asset increases, the
utilization rate for its liquidity pool rises accordingly.
However, a very high utilization rate could create
problems for redemptions when many depositors
try to withdraw at the same time. To minimize this
risk, DeFi platforms set a threshold utilization rate
above which the lending interest rate goes up steeply
to discourage higher utilization. The median utiliza-
tion rate is typically high for stablecoins and low for
volatile assets; however, there have been instances for
23Each DeFi platform has its own interest rate model that deter-
mines loan and deposit rates based on the utilization rate.
O
th
er
c
ry
pt
o
as
se
ts
Median Maximum
Figure 3.10. Decentralized Finance Liquidity Risks
Liquidity could become insufficient during periods of market volatility.
1. Distribution of the Utilization Rate across Assets and Platforms
(Median and maximum value)
St
ab
le
co
in
s
0
60
40
20
80
100
0
60
40
20
80
100
Jan. 2020 July 20 Jan. 21 July 21
2. Liquidity Concentration: Number of Accounts Providing 50 Percent
of Liquidity
(Distribution of liquidity by assets, median and 5th to 95th range)
Liquidity is highly concentrated in a small number of accounts.
0
25
5
10
15
20
Stablecoin
5th to 95th range
Median
Other crypto assets
0
25
5
10
15
20
Jan. 2019 July 19 Jan. 20 July 20 Jan. 21 July 21
Sources: Aave v2; Compound v2; C.R.E.A.M. Finance; The Graph; and IMF staff calculations.
Note: The utilization rate of a crypto asset is the ratio of total loans to total deposits of that asset in the platform.

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International Monetary Fund | April 2022
both types of assets when utilization rates approached
100 percent during periods of market volatility
(Figure 3.10, panel 1).
Liquidity provision is highly concentrated, mak-
ing DeFi platforms ironically less decentralized than
expected.24 On average, half of the deposits are
provided by fewer than 10 accounts, with even more
concentrated in smaller and more volatile crypto assets
(Figure 3.10, panel 2; see also Aramonte, Huang, and
Schrimpf 2021; Gudgeon and others 2020). With
higher concentration, an idiosyncratic withdrawal of
funds by any of those large depositors can have a mate-
rial impact on the liquidity condition of the platform.
This, in turn, can exacerbate liquidity exhaustion, as
illustrated by the occasional spikes in the utilization
rate.25 A more extreme outcome would be equivalent
to a bank run—when participants rush to withdraw
liquidity from the platform.
24The liquidity providers cannot be identified due to DeFis’
anonymous nature.
25A spike can be triggered by other factors, such as changes in the
threshold utilization rate of the interest rate model.
Cyber Risks: A Critical Risk of Decentralized Finance
Cyberattacks increased substantially in mid-2021 and
remain elevated. The attacks are associated mostly with
compromised wallet keys, vulnerabilities in computer
code, and scams by developers (Figure 3.11, panel 1).
Cyberattacks cause large and often persistent losses.
An event analysis shows a substantially adverse impact
of cyberattacks on the excess growth of total value
locked that represents the total value of crypto assets
supplied to the platform, most of which are deposits.26
The estimate suggests that, in most cases, 30 per-
cent of the total value locked is lost or withdrawn
(Figure 3.11, panel 2). Cyberattacks not only steal
assets but also undermine the reputation of a platform,
often triggering withdrawals by depositors as they fear
not being able to redeem their deposits.27 As indicated
by the lower tail of the interquartile range, an entire
platform can collapse in the aftermath of an attack.
26In addition to deposits, total value locked includes governance
tokens (staking tokens) that are locked to the platform.
27When a DeFi platform falls short of liquidity, depositors likely
cannot withdraw, and they lose their assets. Deposits in DeFi
platforms are not eligible for any deposit insurance or central bank
liquidity support measures.
Gross value stolen
Number of incidents (right scale)
MedianInterquartile range
Figure 3.11. Cyberattacks on Decentralized Finance
The frequency and scale of cyberattacks surged in 2021 and remain
elevated.
1. DeFi-Related Cyberattacks
(Millions of US dollars)
0
1,200
300
600
900
0
30
5
10
15
20
25
2020:Q1 20:Q2 20:Q3 20:Q4 21:Q1 21:Q2 21:Q3 21:Q4
In most cases, more than 30 percent of the deposit was lost or
withdrawn after attacks.
2. Cumulative Abnormal Returns after Attacks
(Percent)
–90
0
–60
–30
Days after the incident
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Sources: Chainalysis; CoinGecko; CryptoSec.info; DeFi Llama; Immunefi; Rekt; and IMF staff calculations.
Note: DeFi = decentralized finance.

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International Monetary Fund | April 2022
Efficient but Risky
DeFi has the potential to exhibit cost-efficient finan-
cial intermediation by bypassing and shortcutting the
intermediation chain. However, comparing costs and
prices between DeFi and traditional financial institutions
is complex because the two currently operate in different
ecosystems. To address this issue, price-cost margins and
marginal costs are estimated, taking into account their
distinct cost structures. Following Berger, Klapper, and
Turk-Ariss (2009), prices are proxied by the ratio of total
revenue to total assets, and marginal costs are estimated
using a panel regression model of total cost functions.28
The analysis shows that DeFi has the lowest marginal
cost compared with incumbents in both advanced and
emerging market economies, indicating the highest
cost-efficiency (Figure 3.12, panel 1). The low marginal
28In the empirical approach used, liabilities are an intermediate
input in the production of loans, total assets are the output, and
the revenue associated with the output is interest and noninterest
income. The marginal cost is defined as an incremental cost of addi-
tional loan production, and the margin is the difference between the
price and marginal cost. See Online Annex 3.4 for details.
costs of DeFi reflect their automated and unregu-
lated operation, which contrasts with the high share
of labor and operational cost of traditional financial
institutions—including (at least in part) costs related to
regulatory compliance (Figure 3.12, panel 1).29 However,
DeFi bears high funding costs that likely reflect higher
risks, such as lack of access to central bank liquidity
support, AML/CFT risks, and legal and jurisdictional
uncertainties.
However, DeFi’s low margins raise concern about
underpricing risk. DeFi margins are substantially lower
than those of traditional financial institutions, offering
favorable prices to borrowers (Figure 3.12, panel 1).
DeFi currently must offer relatively high deposit interest
rates while keeping lending margins low to attract
29DeFi platforms can also incur episodic operational costs
surrounding cyberattacks or program bugs. For example, about
$90 million was mistakenly distributed to Compound users as
a result of program bugs after an update on October 1, 2021.
Although the founder made a plea to users to voluntarily return the
tokens, the value of tokens not retrieved would be considered a cost
to the platform.
Funding cost Labor cost
Operational cost Other cost
Margin Price
Marginal cost
Banks (corporate loans)
Banks (retail loans)
DeFi platforms
Figure 3.12. Efficiency and Risks of Decentralized Finance
DeFi has the lowest marginal costs due to the absence of labor and
operational costs.
1. Estimated Marginal Costs and Margins
(Percent)
0
14
2
4
6
8
10
12
DeFi platforms Bank (AE) Nonbank (AE) Bank (EM) Nonbank (EM)
Despite high cost-efficiency, DeFi is exposed to riskier borrowers.
2. Margins and Expected Losses
(Percent)
Pr
ic
e-
co
st
m
ar
gi
ns
0.0
2.5
0.5
1.0
1.5
2.0
Expected loss
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Sources: Aave; CoinGecko; Compound v2; European Banking Authority Risk Dashboard; Fitch Connect; and IMF staff calculations.
Note: In panel 1, the sample is composed of banks from 37 advanced economies (AEs) and 100 emerging markets (EMs); nonbanks from 20 advanced economies
and 26 emerging markets; and two DeFi platforms (Aave and Compound). See Online Annex 3.4 for technical details. In panel 2, expected losses of DeFi platforms
are the estimates from Figure 3.10, panel 2. Each dot represents the average expected loss for banks in a country. DeFi = decentralized finance.

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International Monetary Fund | April 2022
depositors and borrowers. Narrow margins are in part
possible because DeFi does not have to maintain regula-
tory buffers. To assess margins against risk exposure, the
estimated average expected losses of DeFi platforms are
compared with those of banks. This comparison suggests
that DeFi is significantly underpricing the riskiness of its
lending (Figure 3.12, panel 2). Although lower margins
can increase the popularity of DeFi, they come at a cost
of thinner reserve buffers, which builds vulnerabilities
during periods of market stress. At the same time, lower
margins may pose significant competitive pressure to
incumbents absent a (regulatory) level playing field.
Financial Stability and Policy Issues
The acceleration of digitalization in core banking
services brings opportunities and risks. On the one hand,
by strengthening and broadening financial development,
fintechs can support more inclusive economic growth. On
the other, the rapid growth of fintechs raises the risk of
bank disintermediation. This is not necessarily a financial
stability concern if fintechs are subject to appropriate reg-
ulatory oversight to ensure a level playing field. However,
the rapid growth of fintechs does raise financial stability
issues, including a potential buildup of vulnerabilities
in new corners of the financial system and challenges to
adapt regulatory and supervisory rules to new actors.
Regulatory Differences
Neobanks are sometimes subject to simpler and less
comprehensive regulation and supervision. While neo-
banks in most jurisdictions are subject to banking require-
ments, these can be simpler than Basel III rules applicable
to internationally active banks, mainly due to their
current size. Conversely, in some jurisdictions neobanks
operate without a banking license, some are not subject
to liquidity risk requirements, and they may be subject to
different loan classification and lower provisioning. Less
comprehensive requirements may incentivize risk-taking
in loan underwriting and securities investment.
These regulatory approaches may have been designed
to be both conservative and simple for small and tra-
ditional banks. However, as the analysis in this chapter
indicates, neobanks tend to be more aggressive than
traditional banks in terms of loan underwriting, invest-
ment in riskier securities, and liquidity management.
This suggests that although authorities may have targeted
a proportional approach to regulation so as not to hinder
innovation, in practice some of this proportionality is
not sufficiently risk-based to address different business
models and the risk-taking appetite of neobanks.30
Adapting Policies to Address Risks in Neobanks and
Fintech Mortgage Firms
The rapid growth of fintechs worldwide has led to
interconnectedness within the financial sector, which
could exacerbate financial stability challenges. The
neobank case study unveils vulnerabilities across at least
four dimensions: (1) higher risk-taking in retail loan
originations without appropriate provisioning and pricing
standards; (2) higher risk-taking in the securities portfolio
as a way to cross-subsidize their lending business in order
to support its price-competitiveness vis-à-vis traditional
banks; (3) potential underspending in critical functions
(such as AML/CFT and IT/cybersecurity) as they fail
to match market expectations for meaningful efficiency
gains down the road; and (4) liquidity buffers that do not
appear to be well calibrated to neobanks’ less sticky retail
deposit base. In addition, neobanks are providing funding
to traditional banks through the interbank market.
Moreover, a small number of fintech firms provide critical
services (such as cloud services) to financial institutions.
Even if regulation delivers a level playing field
for fintechs and incumbents, the scalability of
technology-enabled business models allows fintechs to
grow fast, putting pressure on incumbents. The compet-
itive pressure on traditional banks can be significant. As
the case study of the US mortgage market shows, there
is strong evidence of a negative impact on banks’ income
as a result of competition from fintechs. Importantly, evi-
dence also shows that banks adopting fintech-like tech-
nologies are less affected. Excessive risk-taking by both
fintechs and incumbents to gain or defend market share
could lead to a fast buildup of systemic risk (Vives 2019).
The rapidly changing risks in fintechs require policy
action to tighten and clarify fintech regulation, as well
as enhanced monitoring of incumbents, which might be
more vulnerable under pressure from rapid fintech devel-
opment. First, prudential regulations at both the entity
and group levels should be reviewed to address fintechs’
key risks in a forward-looking manner. This will likely
mean more robust capital, liquidity, and operational
risk-management requirements, commensurate with
30Many neobanks are not subject to group-wide supervision,
which creates regulatory arbitrage opportunities.

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International Monetary Fund | April 2022
the risk taken by neobanks in several jurisdictions.
Second, the health of technology laggards and smaller
banks could be particularly at risk as they may not have
the resources and know-how to adapt to technological
changes. This may require supervisors to closely monitor
less technologically advanced incumbents.
Regulating Decentralized Finance
DeFi poses unique challenges to regulators. DeFi’s
elevated market, liquidity, and cyber risks may need
adjustment to the regulatory perimeter, but DeFi’s
anonymity, lack of a centralized governance body, and
legal uncertainties render the traditional approach to
regulation ineffective.
As DeFi, stablecoins, and traditional financial enti-
ties have grown ever more interconnected, enhanced
regulatory surveillance and globally consistent regu-
latory frameworks will be necessary. Stablecoins are
backed or collateralized by cash and financial instru-
ments, and regulated financial institutions are increas-
ing their exposure to and funding from stablecoins
(Aramonte, Huang, and Schrimpf 2021). This linkage
can lead to stronger interconnectedness between DeFi
and the financial sector. Basel Committee on Banking
Supervision (BCBS) proposals on banks’ crypto asset
exposures are a significant step toward global standards
to help address some cross-border issues.31
As a first step, regulation should focus on some
elements of the crypto ecosystem that have enabled the
development of DeFi. These include stablecoin issuers
(which define technical specification and use cases);
centralized crypto exchanges and hosted wallet service
providers (which connect crypto markets with the
broader financial system); and reserve managers, net-
work administrators, and market makers (which play
important roles in operationalization and stability).
These entities would benefit from robust and com-
prehensive national regulatory frameworks delivered
through common global standards by standard-setting
bodies. Those centralized entities in the crypto asset
ecosystem could be an effective liaison for regulators to
address the risk of rapid DeFi growth.
31In 2021, the BCBS consulted on a preliminary proposal for a
prudential treatment of banks’ crypto asset exposures. The proposed
standards reflect the high risk of some crypto assets, while taking a
more proportional approach to those that are anchored on real-world
assets. After this initial public consultation, the Committee has
reviewed the comments received and is now working to further spec-
ify a proposed prudential treatment, with a view to issuing a further
consultative paper by mid-2022.
As a second step, authorities can directly regu-
late key functions within DeFi. To manage the risks
generated by protocol developers, measures could
include public-private collaboration on code regula-
tion through either ex ante guidelines on operational
and risk parameters (including operational and cyber
resilience) or ex post code reviews and audits that can
identify areas vulnerable to risk and help deliver policy
objectives. Ex ante measures can be combined with
greater disclosure and user education to help identify
platform-specific risks, closing the information gap
between retail and institutional investors.
Authorities should encourage DeFi platforms to
adopt robust governance through industry codes and
build effective public-private collaboration to establish
self-regulatory organizations. A transparent and credible
governance system could improve risk management,
facilitate good conduct of financial transactions, and
eventually attract more users and capital to the plat-
forms. Such a governance system could be a natural
entry point for regulators to interact either directly
or through the development of industry codes or
self-regulatory organizations. For example, their gov-
ernance token holders can form decentralized autono-
mous organizations with voting rights, like traditional
securities.32 These organizations may provide authorities
with a conduit for regulatory oversight, ensuring that
DeFi platforms enhance disclosure and have suitable
controls. Much as in traditional securities markets,
self-regulatory organizations for centralized crypto
exchanges would lead to more robust listing standards
for (tokens of ) DeFi platforms and thereby improve
their governance and quality. Regulators should monitor
the effectiveness of industry codes and self-regulation
and enhance supervision intensity when necessary.
Enforcing regulations—including restrictions—in
DeFi markets is challenging, as experience from crypto
markets shows.33 One potential approach is to restrict
the exposure of regulated firms to DeFi markets
(especially markets not subject to proper regulation or
self-regulation), which could slow the pace of growth
while addressing the risks of interconnectedness with
regulated markets.
32In some jurisdictions, such as the state of Wyoming in the
United States, decentralized autonomous organizations are consid-
ered legal entities.
33Despite the implementation of restrictions, an estimated
1.7 million Egyptians hold crypto assets (TripleA 2022). Many
crypto asset service providers operate offshore; users can take advan-
tage of virtual private networks to obscure their location, demon-
strating the difficulty in enforcing regulations.

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International Monetary Fund | April 2022
References
Aramonte, Sirio, Wenqian Huang, and Andreas Schrimpf. 2021
“DeFi Risks and the Decentralization Illusion.” BIS Quarterly
Review (December).
Berger, Allen N., Leora F. Klapper, and Rima Turk-Ariss. 2009.
“Bank Competition and Financial Stability: An Empirical
Evaluation.” Journal of Financial Services Research 35:
99–118.
Boot, Arnoud, Peter Hoffmann, Luc Laeven, and Lev Ratnovski.
2021. “Fintech: What’s Old, What’s New?” Journal of Finan-
cial Stability 53.
Buchak, Greg, Gregor Matvos, Tomasz Piskorski, and Amit Seru.
2018. “Fintech, Regulatory Arbitrage, and the Rise of Shadow
Banks.” Journal of Financial Economics 130 (3): 453–83.
Drakopoulos, Dimitris, Fabio Natalucci, and Evan Papageorgiou.
2021. “Crypto Boom Poses New Challenges to Financial Sta-
bility.” IMFBlog, International Monetary Fund, October 1.
https:// blogs .imf .org/ 2021/ 10/ 01/ crypto -boom -poses -new
-challenges -to -financial -stability/ .
Fuster, Andreas, Matthew Plosser, Philipp Schnabl, and James
Vickery. 2019. “The Role of Technology in Mortgage Lend-
ing.” Review of Financial Studies 32 (5): 1854–899.
Garrido, José, Yan Liu, Joseph Sommer, and Juan Sebastián
Viancha. 2022. “Keeping Pace with Change: Fintech and
the Evolution of Commercial Law.” IMF Fintech Notes
2022/001, International Monetary Fund, Washington, DC.
Gudgeon, Lewis, Sam Werner, Daniel Perez, and William J.
Knottenbelt. 2020. “DeFi Protocols for Loanable Funds:
Interest Rates, Liquidity and Market Efficiency.” In AFT
’20: Proceedings of the 2nd ACM Conference on Advances in
Financial Technologies. New York: Association for Comput-
ing Machinery.
Haksar, Vikram, Yan Carriere-Swallow, Emanuel Kopp, Gabriel
Quiros, Emran Islam, Andrew Giddings, and Kathleen Kao.
2021. “Toward a Global Approach to Data in the Digital
Age.” IMF Staff Discussion Note 2021/005, International
Monetary Fund, Washington, DC.
International Monetary Fund (IMF). 2018. “The Bali Fintech
Agenda.” IMF Policy Paper, Washington, DC.
Jagtiani, Julapa, Lauren Lambie-Hanson, and Timothy
Lambie-Hanson. 2021. “Fintech Lending and Mortgage
Credit Access.” Journal of FinTech 1 (1): 2050004.
TripleA. 2022. “Cryptocurrency Information about Egypt.”
https:// triple -a .io/ crypto -ownership -egypt/ .
UK Finance. 2021. “Fraud–The Facts 2021: The Definitive
Overview of Payment Industry Fraud.” London.
Vives, Xavier. 2019. “Digital Disruption in Banking.” Annual
Review of Financial Economics 11:243–72.

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GLOBAL FINANCIAL STABILITY REPORT APRIL 2022
IN THIS ISSUE:
CHAPTER 1
The Financial Stability Implications
of the War in Ukraine
CHAPTER 2
The Sovereign-Bank
Nexus in Emerging Markets:
A Risky Embrace
CHAPTER 3
The Rapid Growth of Fintech:
Vulnerabilities and Challenges
for Financial Stability

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International Monetary Fund | October 2017 iii
CONTENTS
Assumptions and Conventions vi
Further Information and Data vii
Preface viii
Foreword ix
Executive Summary x
IMF Executive Board Discussion Summary xiv
Chapter 1 Is Growth at Risk? 1
Financial Stability Overview 1
Large Systemic Banks and Insurers: Adapting to the New Environment 2
Monetary Policy Normalization: A Two-Sided Risk 17
Has the Search for Yield Gone Too Far? 23
The Rise in Leverage 32
Could Rising Medium-Term Vulnerabilities Derail the Global Recovery? 42
Box 1.1. A Widening Divergence between Financial and Economic Cycles 47
Box 1.2. Cyberthreats as a Financial Stability Risk 49
References 51
Chapter 2 Household Debt and Financial Stability 53
Summary 53
Introduction 54
How Does Household Debt Affect Macroeconomic and Financial Stability? 56
Developments in Household Debt around the World 58
Financial Stability Risks of Household Debt: Empirical Analysis 62
Conclusions and Policy Implications 70
Box 2.1. Long-Term Growth and Household Debt 72
Box 2.2. Distributional Aspects of Household Debt in China 73
Box 2.3. A Comparison of US and Canadian Household Debt 75
Box 2.4. The Nexus between Household Debt, House Prices, and Output 77
Box 2.5. The Impact of Macroprudential Policies on Household Credit 79
Annex 2.1. Data Sources 81
Annex 2.2. Methodology 84
References 87
Chapter 3 Financial Conditions and Growth at Risk 91
Summary 91
Introduction 92
Financial Conditions and Risks to Growth: Conceptual Issues 93
How Do Changes in Financial Conditions Indicate Risks to Growth? 96
How Well Do Changes in Financial Conditions Forecast Downside Risks to Growth? 100
Policy Implications 108
Annex 3.1. Financial Vulnerabilities and Growth Hysteresis in Structural Models 109
Annex 3.2. Estimating Financial Conditions Indices 113

iv International Monetary Fund | October 2017
G LO B A L F I N A N C I A L S TA B I L I T Y R E P O R T: I S G R O W T H AT R I S K ?
Annex 3.3. The Conditional Density of Future GDP Growth 115
References 116
Tables
Table 1.1. Sovereign and Nonfinancial Private Sector Debt-to-GDP Ratios 34
Annex Table 2.1.1. Countries Included in the Sample for Household Debt and Data Sources 81
Annex Table 2.1.2. Household Survey Data Sources 82
Annex Table 2.1.3. Description of Explanatory Variables Used in the Chapter 83
Annex Table 2.2.1. Logit Analysis: Probability of Systemic Banking Crisis 84
Annex Table 2.2.2. Panel Regression Estimates for Three-Year-Ahead Growth Regression on
Household Debt and Policy Interaction Variables 86
Table 3.1. Forecast of GDP Growth Distribution for the Global Financial Crisis with and
without Financial Conditions Indices 104
Table 3.2. Market Consensus Forecasts for the Global Financial Crisis Were Considerably
More Optimistic Than Forecasts Based on Financial Conditions 104
Table 3.3. Forecast of GDP Growth Distribution for the Global Financial Crisis: Comparing
Partitioned and Univariate Financial Conditions Indices with Autoregressions 105
Annex Table 3.2.1. Country Coverage 113
Annex Table 3.2.2. Data Sources 114
Annex Table 3.2.3. Partitioning of Financial Indicators into Groups 115
Figures
Figure 1.1. Global Financial Stability Map: Risks and Conditions 2
Figure 1.2. Global Financial Stability Map: Assessment of Risks and Conditions 3
Figure 1.3. Search for Yield, Asset Valuations, and Volatility 4
Figure 1.4. Global Systemically Important Banks: Significance and Business Model Snapshot 6
Figure 1.5. Global Systemically Important Banks: Capital, Liquidity, and Legacy Challenges 7
Figure 1.6. Global Systemically Important Banks: Market Activity 9
Figure 1.7. Global Systemically Important Banks’ International Activity 10
Figure 1.8. Global Systemically Important Banks: Financial Performance Gaps 12
Figure 1.9. Life Insurance Companies’ Profitability and Capital 13
Figure 1.10. Changes in Life Insurance Companies’ Business Models 14
Figure 1.11. Life Insurers’ Market Valuations and Risk Outlook 16
Figure 1.12. Simulated Mark-to-Market Shocks to Assets and Liabilities 17
Figure 1.13. Central Bank Balance Sheets and the Sovereign Sector 19
Figure 1.14. Policy Rates, 10-Year Government Bond Yields, and Term Premiums 20
Figure 1.15. Emerging Market Economy Capital Flows 22
Figure 1.16. Global Fixed Income Markets and US Corporate Credit Investor Base 24
Figure 1.17. Emerging Market Economies: Debt Issuance, Portfolio Flows, and Asset Prices 25
Figure 1.18. Low-Income Country External Borrowing and Vulnerabilities 26
Figure 1.19. US and Emerging Market Corporate Bond Spread Decomposition and Leverage 27
Figure 1.20. Long-Term Drivers of the Low-Volatility Regime 29
Figure 1.21. Leveraged and Volatility-Targeting Strategies 30
Figure 1.22. Vulnerability of the US Corporate Credit Investor Base to Shocks 31
Figure 1.23. Group of Twenty Nonfinancial Sector Credit Trends 33
Figure 1.24. Group of Twenty Nonfinancial Private Sector Borrowing 35
Figure 1.25. Group of Twenty Nonfinancial Private Sector Credit and Debt Service Ratios 36
Figure 1.26. Chinese Banking System Developments 38
Figure 1.27. China: Regulatory Tightening Has Helped Contain Financial Sector Risks 39
Figure 1.28. Chinese Banks: Financial Policy Tightening and Credit Growth Capacity 40
Figure 1.29. Bank Profitability and Liquidity Indicators 41
Figure 1.30. Global Financial Dislocation Scenario 43
Figure 1.31. Emerging Market Economy External Vulnerabilities and Corporate Leverage 45
Figure 1.1.1. Financial and Economic Cycles 48

C o n t e n t s
International Monetary Fund | October 2017 v
Figure 2.1. Household Debt-to-GDP Ratio in Advanced and Emerging Market Economies 54
Figure 2.2. First- and Second-Round Effects of the Buildup of Household Debt on Financial Stability 57
Figure 2.3. Growth and Composition of Household Debt by Region 60
Figure 2.4. Household Debt: Evidence from Cross-Country Panel Data 61
Figure 2.5. Effects of Household Debt on GDP Growth and Consumption 64
Figure 2.6. Effects of Household Debt on GDP Growth: Robustness Tests 65
Figure 2.7. Micro-Level Evidence Corroborating the Macro Impact 66
Figure 2.8. Banking Crises and the Role of Household Debt 67
Figure 2.9. Bank Equity Returns and Household Debt 68
Figure 2.10. The Impact of Household Debt by Country and Institutional Factors 69
Figure 2.1.1. Long-Term per Capita GDP Growth and Household Debt 72
Figure 2.2.1. Characteristics of China’s Household Debt 73
Figure 2.3.1. US and Canadian Household Debt Developments and Characteristics 75
Figure 2.4.1. Panel Vector Autoregression Dynamic Analysis 77
Figure 2.4.2. Consumption Response to House Prices 78
Figure 2.5.1. Macroprudential Policy Tools and Household Credit Growth 79
Annex Figure 2.1.1. Loan Characteristics, Rules, and Regulations 82
Figure 3.1. Tighter Financial Conditions Forecast Greater Downside Tail Risk to Global Growth 97
Figure 3.2. Risk of Severe Recessions Is Especially Sensitive to a Tightening of Financial Conditions
in Major Advanced and Emerging Market Economies 98
Figure 3.3. In Emerging Market Economies, Changes in Financial Conditions Also Affect Upside Risks 99
Figure 3.4. Higher Price of Risk Is a Significant Predictor of Downside Growth Risks within One Year 101
Figure 3.5. Rising Leverage Signals Higher Downside Growth Risks at Longer Time Horizons 102
Figure 3.6. Waning Global Risk Appetite Signals Imminent Downside Risks to Growth 102
Figure 3.7. Probability Densities of GDP Growth for the Depths of the Global Financial Crisis 103
Figure 3.8. In-Sample and Recursive Out-of-Sample Quantile Forecasts: One Quarter Ahead 106
Figure 3.9. In-Sample and Recursive Out-of-Sample Quantile Forecasts: Four Quarters Ahead 107
Annex Figure 3.1.1. Conditional Densities of Growth with High and Low Asset
Prices—One-Period-Ahead Forecasts 110
Annex Figure 3.1.2. One-Period-Ahead GDP and Financial Conditions 111
Annex Figure 3.1.3. Asset Prices and Credit Aggregates before and after a Financial Crisis 111
Annex Figure 3.1.4. Simple Debt Tax Ameliorates Risk of Leverage-Induced Recessions 112

vi International Monetary Fund | October 2017
ASSUMPTIONS AND CONVENTIONS
The following conventions are used throughout the Global Financial Stability Report (GFSR):
. . . to indicate that data are not available or not applicable;
— to indicate that the figure is zero or less than half the final digit shown or that the item does not exist;
– between years or months (for example, 2016–17 or January–June) to indicate the years or months covered,
including the beginning and ending years or months;
/ between years or months (for example, 2016/17) to indicate a fiscal or financial year.
“Billion” means a thousand million.
“Trillion” means a thousand billion.
“Basis points” refers to hundredths of 1 percentage point (for example, 25 basis points are equivalent to ¼ of
1 percentage point).
If no source is listed on tables and figures, data are based on IMF staff estimates or calculations.
Minor discrepancies between sums of constituent figures and totals shown reflect rounding.
As used in this report, the terms “country” and “economy” do not in all cases refer to a territorial entity that is a state
as understood by international law and practice. As used here, the term also covers some territorial entities that are
not states but for which statistical data are maintained on a separate and independent basis.
The boundaries, colors, denominations, and any other information shown on the maps do not imply, on the part
of the International Monetary Fund, any judgment on the legal status of any territory or any endorsement or
acceptance of such boundaries.

1CHAPTE
R
International Monetary Fund | October 2017 vii
This version of the Global Financial Stability Report (GFSR) is available in full through the IMF eLibrary (www.
elibrary.imf.org) and the IMF website (www.imf.org).
The data and analysis appearing in the GFSR are compiled by the IMF staff at the time of publication. Every effort
is made to ensure, but not guarantee, their timeliness, accuracy, and completeness. When errors are discovered,
there is a concerted effort to correct them as appropriate and feasible. Corrections and revisions made after publica-
tion are incorporated into the electronic editions available from the IMF eLibrary (www.elibrary.imf.org) and on
the IMF website (www.imf.org). All substantive changes are listed in detail in the online tables of contents.
For details on the terms and conditions for usage of the contents of this publication, please refer to the IMF
Copyright and Usage website, www.imf.org/external/terms.htm.
FURTHER INFORMATION AND DATA

viii International Monetary Fund | October 2017
The Global Financial Stability Report (GFSR) assesses key risks facing the global financial system. In normal
times, the report seeks to play a role in preventing crises by highlighting policies that may mitigate systemic
risks, thereby contributing to global financial stability and the sustained economic growth of the IMF’s member
countries.
The analysis in this report has been coordinated by the Monetary and Capital Markets (MCM) Department
under the general direction of Tobias Adrian, Director. The project has been directed by Peter Dattels and Dong
He, both Deputy Directors, as well as by Claudio Raddatz and Matthew Jones, both Division Chiefs. It has ben-
efited from comments and suggestions from the senior staff in the MCM Department.
Individual contributors to the report are Ali Al-Eyd, Zohair Alam, Adrian Alter, Sergei Antoshin, Magally
Bernal, André Leitão Botelho, Luis Brandão-Marques, Jeroen Brinkhoff, John Caparusso, Sally Chen, Shiyuan
Chen, Yingyuan Chen, Charles Cohen, Claudia Cohen, Fabio Cortes, Dimitris Drakopoulos, Kelly Eckhold,
Martin Edmonds, Jesse Eiseman, Jennifer Elliott, Aquiles Farias, Alan Xiaochen Feng, Caio Ferreira, Tamas
Gaidosch, Rohit Goel, Hideo Hashimoto, Sanjay Hazarika, Dong He, Geoffrey Heenan, Dyna Heng, Paul
Hiebert, Henry Hoyle, Nigel Jenkinson, David Jones, Mitsuru Katagiri, Will Kerry, Jad Khallouf, Robin Koepke,
Romain Lafarguette, Tak Yan Daniel Law, Feng Li, Yang Li, Peter Lindner, Xiaomeng Lu, Sheheryar Malik,
Rebecca McCaughrin, Kei Moriya, Aditya Narain, Machiko Narita, Vladimir Pillonca, Thomas Piontek,
Breanne Rajkumar, Mamoon Saeed, Luca Sanfilippo, Jochen Schmittmann, Yves Schüler, Dulani Seneviratne,
Juan Solé, Ilan Solot, Yasushi Sugayama, Jay Surti, Narayan Suryakumar, Nico Valckx, Francis Vitek, Changchun
Wang, Jeffrey Williams, Christopher Wilson, and Xinze Yao. Magally Bernal, Breanne Rajkumar, and Claudia
Cohen were responsible for word processing.
Gemma Diaz from the Communications Department led the editorial team and managed the report’s produc-
tion with support from Linda Kean and editorial assistance from Sherrie Brown, Lorraine Coffey, Susan Graham,
Lucy Scott Morales, Nancy Morrison, Katy Whipple, AGS, and Vector.
This particular issue of the GFSR draws in part on a series of discussions with banks, securities firms, asset
management companies, hedge funds, standard setters, financial consultants, pension funds, central banks, national
treasuries, and academic researchers.
This GFSR reflects information available as of September 22, 2017. The report benefited from comments and
suggestions from staff in other IMF departments, as well as from Executive Directors following their discussion of
the GFSR on September 21, 2017. However, the analysis and policy considerations are those of the IMF staff and
should not be attributed to Executive Directors or their national authorities.
PREFACE

T
wice a year, the Global Financial Stability
Report (GFSR) assesses the degree to which
developments in the financial sector may
affect future economic conditions by
analyzing macro-financial linkages and then identifies
policies to mitigate risks to growth from the financial
sector. At the current juncture, investor risk appetite
is buoyant globally: since the last report in April,
funding conditions have continued to improve, asset
return volatility has receded to multiyear lows across
markets, and global capital flows have surged. This
easing of financial conditions has supported global
growth and financial inclusion, with credit being
allocated to benefit a broad range of borrowers.
These favorable conditions create a window of
opportunity to strengthen the financial system that
should be seized, since experience has taught us that
it is during times of easy financial conditions that
vulnerabilities build.
Chapter 1 of this GFSR documents how the con-
tinuation of monetary accommodation in advanced
economies—necessary to support activity and boost
inflation—is associated with rising asset valuations
and higher leverage, and how this environment makes
the system more vulnerable to future shocks. Chapter
2 focuses on household leverage, showing that ample
credit growth portends benign conditions in the
near term but larger downside risks in the medium
term—and thus creates an intertemporal tradeoff.
Chapter 3 takes this logic a step further and directly
links the easing of financial conditions to downside
risks to GDP growth. Easy financial conditions fuel
growth in the shorter term, but when those condi-
tions are coupled with a buildup in leverage, risks to
growth rise in the medium term. In fact, we propose
to measure financial stability by a measure of Growth
at Risk, defined as the value at risk of future GDP
growth as a function of financial vulnerability.
The analysis in all three chapters underscores that
some of the factors that have contributed to recent
gains in financial stability could put growth at risk in
the medium term in the absence of appropriate policies
to address rising financial vulnerabilities. Macropru-
dential policies, such as those that address underwriting
standards, are the primary tool for guarding against
future risks to growth from the global financial system.
Now is the time to further strengthen that system,
particularly by focusing on nonbank institutions, whose
vulnerabilities are rising. Macroprudential policies that
mitigate the buildup of medium-term risks can also
help to better balance monetary policy tradeoffs.
Whereas vulnerabilities are rising in the nonbank
financial system, the safety of the global systemically
important banks (GSIBs) has improved significantly.
Those banks have more capital and more liquidity and
are subject to tighter supervision, thanks to the pivotal
reforms undertaken after the 2008 global financial
crisis. Yet some GSIBs still struggle to adapt their
business models to ensure their continued health and
profitability, which is critical if they are to fulfill their
primary mandate: lending to the real economy. A
review of the unintended consequences of the postcri-
sis regulatory reforms will likely lead to some stream-
lining in the implementation of banking regulations,
but it is essential that the overall high level of capital
and liquidity be preserved, regulatory uncertainty be
avoided, and the global financial regulatory reform
agenda be completed. Equally essential is continuing
international regulatory cooperation.
Tobias Adrian
Financial Counsellor
International Monetary Fund | October 2017 ix
FOREWORD

x International Monetary Fund | October 2017
Near-Term Risks Are Lower
The global financial system continues to strengthen
in response to extraordinary policy support, regulatory
enhancements, and the cyclical upturn in growth. The
health of banks in many advanced economies contin-
ues to improve, as progress has been made in resolv-
ing some weaker banks, while a majority of systemic
institutions are adjusting business models and restoring
profitability. The upswing in global economic activity,
discussed in the October 2017 World Economic Outlook
(WEO), has boosted market confidence while reducing
near-term threats to financial stability.
But beyond these recent improvements, the environ-
ment of continuing monetary accommodation—neces-
sary to support activity and boost inflation—is also
leading to rising asset valuations and higher leverage.
Financial stability risks are shifting from the bank-
ing system toward nonbank and market sectors of the
financial system. These developments and risks call
for delicately balancing the eventual normalization of
monetary policies, while avoiding a further buildup of
financial risks outside the banking sector and address-
ing remaining legacy problems.
The Two Sides of Monetary Policy
Normalization
The baseline path for the global economy, envisaged
by central banks and financial markets, foresees contin-
ued support from accommodative monetary policies, as
inflation rates are expected to recover only slowly. Thus,
the gradual process of normalizing monetary policies
is likely to take several years. Too fast a pace of nor-
malization would remove needed support for sustained
recovery and desired increases in core inflation across
major economies. Unconventional monetary policies
and quantitative easing have forced substantial portfolio
adjustments in the private sector and across borders,
making the adjustment of financial markets much less
predictable than in previous cycles. Abrupt or ill-timed
shifts could cause unwanted turbulence in financial
markets and reverberate across borders and markets. Yet
the prolonged monetary support envisaged for the major
economies may lead to the buildup of further financial
excesses. As the search for yield intensifies, vulnerabilities
are shifting to the nonbank sector, and market risks are
rising. There is too much money chasing too few yield-
ing assets: less than 5 percent ($1.8 trillion) of the cur-
rent stock of global investment-grade fixed-income assets
yields over 4 percent, compared with 80 percent ($15.8
trillion) before the crisis. Asset valuations are becoming
stretched in some markets as investors are pushed out of
their natural risk habitats, and accept higher credit and
liquidity risk to boost returns.
At the same time, indebtedness among the major
global economies is increasing. Leverage in the non-
financial sector is now higher than before the global
financial crisis in the Group of Twenty economies as
a whole. While this has helped facilitate the economic
recovery, it has left the nonfinancial sector more vul-
nerable to changes in interest rates. The rise in leverage
has led to a rise in private sector debt service ratios in
several of the major economies, despite the low level
of interest rates. This is stretching the debt servicing
capacity of weaker borrowers in some countries and
sectors. Debt servicing pressures and debt levels in the
private nonfinancial sector are already high in several
major economies (Australia, Canada, China, Korea),
increasing their sensitivity to tighter financial condi-
tions and weaker economic activity.
The key challenge confronting policymakers is to
ensure that the buildup of financial vulnerabilities is
contained while monetary policy remains supportive
of the global recovery. Otherwise, rising debt loads
and overstretched asset valuations could undermine
market confidence in the future, with repercussions
that could put global growth at risk. This report exam-
ines such a downside scenario, in which a repricing
of risks leads to sharp increases in credit costs, falling
asset prices, and a pullback from emerging markets.
The economic impact of this tightening of global
financial conditions would be significant (about one-
third as severe as the global financial crisis) and more
broad-based (global output would fall 1.7 percent
relative to the WEO baseline with varying cross-coun-
try effects). Monetary normalization would go into
EXECUTIVE SUMMARY

e x e C u t I v e s u M M a r y
International Monetary Fund | October 2017 xi
reverse in the United States and would stall elsewhere.
Emerging market economies would be disproportion-
ately affected, resulting in an estimated $100 billion
reduction in portfolio flows over four quarters. Bank
capital would take the biggest hit where leverage is
highest and where banks are most exposed to the
housing and corporate sectors.
Deleveraging in China: Challenges Ahead
Steady growth in China and financial policy tighten-
ing in recent quarters have eased concerns about a
near-term slowdown and negative spillovers to the
global economy. However, the size, complexity, and
pace of growth in China’s financial system point to
elevated financial stability risks. Banking sector assets,
at 310 percent of GDP, have risen from 240 percent
of GDP at the end of 2012. Furthermore, the grow-
ing use of short-term wholesale funding and “shadow
credit” to firms has increased vulnerabilities at banks.
Authorities face a delicate balance between tightening
financial sector policies and slowing economic growth.
Reducing the growth of shadow credit even modestly
would weigh on the profitability and broader provision
of credit by small and medium-sized banks.
Global Banks’ Health Is Improving
The health of global systemically important banks
(GSIBs) continues to improve. Balance sheets are stron-
ger because of improved capital and liquidity buffers,
amid tighter regulation and heightened market scrutiny.
Considerable progress has been made in addressing
legacy issues and restructuring challenges. At the same
time, while many banks have strengthened their profit-
ability by reorienting business models, several continue
to grapple with legacy issues and business model chal-
lenges. Banks representing about $17 trillion in assets,
or about one-third of the GSIB total, may continue
to generate unsustainable returns, even in 2019. As
problems in even a single GSIB could generate systemic
stress, supervisory actions should remain focused on
business model risks and sustainable profitability. Life
insurers have also been adapting their business strate-
gies in the low-yield environment following the global
financial crisis. They have done this by reducing legacy
exposures, steering the product mix away from high
guaranteed returns, and seeking higher yields in invest-
ment portfolios. Meanwhile, supervisors need to moni-
tor rising exposure to market and credit risks.
Policymakers Must Take Proactive Measures
Policymakers must take advantage of the improving
global outlook and avoid complacency by addressing
rising medium-term vulnerabilities.
• Policymakers and regulators should fully address
crisis legacy problems and require banks and insur-
ance companies to strengthen their balance sheets
in advanced economies. This includes putting a
resolution framework for international banks into
operation, focusing on risks from weak bank busi-
ness models to ensure sustainable profitability, and
finalizing Basel III. Regulatory frameworks for life
insurers should be enhanced to increase reporting
transparency and incentives to build resilience. A
global and coordinated policy response is needed for
resilience to cyberattacks (see Box 1.2).
• Major central banks should ensure a smooth
normalization of monetary policy through well-
communicated plans on unwinding their holdings
of securities and guidance on prospective changes to
policy frameworks. Providing clear paths for policy
changes will help anchor market expectations and
ward off undue market dislocations or volatility.
• Financial authorities should deploy macroprudential
measures, and consider extending the boundary of
such tools, to curb rising leverage and contain grow-
ing risks to stability. For instance, borrower-based
measures should be introduced and/or tightened to
slow fast-growing overvalued segments, and bank
stress tests must assume more stressed asset valua-
tions. Capital requirements should be increased for
banks that are more exposed to vulnerable borrowers
to act as a cushion for already accumulated expo-
sures and incentivize banks to grant new loans to
less risky sectors.
• Regulation of the nonbank financial sector should
be strengthened to limit risk migration and excessive
capital market financing. Transition to risk-based
supervision should be accelerated, and harmonized
regulation of insurance companies—with emphasis
on capital—should be introduced. Tighter micro-
prudential requirements should be implemented in
highly leveraged segments.
• Debt overhangs—especially among the largest
borrowers as potential originators of shocks—must
be addressed. Discouraging further debt buildup
through measures that encourage business invest-
ment and discourage debt financing will help curb
financial risk taking.

xii International Monetary Fund | October 2017
G LO B A L F I N A N C I A L S TA B I L I T Y R E P O R T: I S G R O W T H AT R I S K ?
• Emerging market economies should continue to
take advantage of supportive external conditions
to enhance their resilience, including by continu-
ing to strengthen external positions where needed,
and reduce corporate leverage where it is high. This
would put these economies in a better position to
withstand a reduction in capital inflows as a result
of monetary normalization in advanced economies
or waning global risk appetite. Similarly, frontier
market and low-income-country borrowers should
develop the institutional capacity to deal with risks
from the issuance of marketable securities, including
formulating comprehensive medium-term debt man-
agement strategies. This will enable them to take
advantage of broader financial market development
and access, while containing the associated risks.
• In China, the authorities have taken welcome steps to
address risks in the financial system, but there is still
work to do. Vulnerabilities will be difficult to address
without slower credit growth. Recent policies to
improve the risk management and transparency of the
banking system and reduce the buildup of maturity
and liquidity transformation risks in banks’ shadow
credit activities are essential and must continue. How-
ever, policies should also target balance sheet vulnera-
bilities at weak banks. The government’s commitment
to reducing corporate leverage is welcome and should
remain a priority as part of a broader effort to insulate
the economy against slower credit growth.
• Although significant progress has been made in
developing the postcrisis policy response, progress
remains uneven across the various sectors, with
several design and implementation issues remain-
ing outstanding. Ensuring that the reform mea-
sures are completed and implemented is essential
to minimize the likelihood of another disruptive
crisis. Completing the reform agenda will also allow
policymakers to conduct a comprehensive evalua-
tion of the impact of the reforms and fine-tune the
agreed measures. This will allow them to address
any material unintended effects their cumulative
implementation might have on the provision of key
financial services. This is critical to provide contin-
ued assurance that reforms have delivered on their
objectives and to stave off emerging pressures to roll
back these measures, which would only make the
financial system more vulnerable.
• Finally, implementation of structural reforms and
supportive fiscal policies (as examined in Scenario
Box 1 of the October 2017 World Economic Outlook)
would lift global growth and generate positive eco-
nomic spillovers, reinforcing financial policy efforts.
Household Debt and Economic Growth
Chapter 2 examines the short- and medium-term
implications for economic growth and financial stability
of the past decades’ rise in household debt. The chapter
documents large differences in household debt-to-GDP
ratios across countries but a common increasing trajec-
tory that was moderated but not reversed by the global
financial crisis. In advanced economies, with notable
exceptions, household debt to GDP increased gradu-
ally, from 35 percent in 1980 to about 65 percent in
2016, and has kept growing since the global financial
crisis, albeit more slowly. In emerging market econo-
mies, the same ratio is still much lower, but increased
relatively faster over a shorter period, from 5 percent in
1995 to about 20 percent in 2016. Moreover, the rise
has been largely unabated in recent years. The chapter
finds a trade-off between a short-term boost to growth
from higher household debt and a medium-term risk to
macroeconomic and financial stability that may result
in lower growth, consumption, and employment and a
greater risk of banking crises. This trade-off is stronger
when household debt is higher and can be attenuated
by a combination of good policies, institutions, and
regulations. These include appropriate macroprudential
and financial sector policies, better financial supervision,
less dependence on external financing, flexible exchange
rates, and lower income inequality.
Financial Conditions Can Predict Growth
The global financial crisis showed policymakers
that financial conditions offer valuable information
about risks to future growth and provide a basis for
targeted preemptive action. Chapter 3 develops a new
macroeconomic measure of financial stability by link-
ing financial conditions to the probability distribution
of future GDP growth and applies it to a set of 21
major advanced and emerging market economies. The
chapter shows that changes in financial conditions
shift the whole distribution of future GDP growth.
Wider risk spreads, rising asset price volatility, and
waning global risk appetite are significant predictors
of increased downside risks to growth in the near
term, and higher leverage and credit growth provide

e x e C u t I v e s u M M a r y
International Monetary Fund | October 2017 xiii
relevant signals of such risks in the medium term.
Today’s prevailing low funding costs and financial
market volatility support a sanguine view of risks to
the global economy in the near term. But increasing
leverage signals potential risks down the road, and
a scenario of a rapid decompression in spreads and
volatility could significantly worsen the risk outlook
for global growth. A retrospective real-time analysis
of the global financial crisis shows that forecasting
models augmented with financial conditions would
have assigned a considerably higher likelihood to
the economic contraction that followed than those
based on recent growth alone. This confirms that the
analytical approach developed in the chapter can be a
significant addition to policymakers’ macro-financial
surveillance toolkit.

xiv International Monetary Fund | October 2017
E
xecutive Directors broadly shared the assess-
ment of global economic prospects and
risks. They observed that global activity has
strengthened further and is expected to rise
steadily into next year. The pickup is broad based
across countries, driven by investment and trade. Nev-
ertheless, the recovery is not complete, with medium-
term global growth remaining modest, especially
in advanced economies and fuel exporters. In most
advanced economies, inflation remains subdued amid
weak wage growth, while slow productivity growth and
worsening demographic profiles weigh on medium-
term prospects. Meanwhile, several emerging markets
and developing economies continue to adjust to a
range of factors, including lower commodity revenues.
Directors noted that, while risks are broadly bal-
anced in the near term, medium-term risks remain
skewed to the downside, with rising financial vulnera-
bilities. These include the possibility of a sudden tight-
ening of global financial conditions, a rapid increase in
private sector debt in key emerging market economies,
low bank profitability and pockets of still-elevated non-
performing loan ratios, and policy uncertainty about
financial deregulation. Directors also pointed to risks
associated with inward-looking policies, rising geopo-
litical tensions, and weather-related factors.
Given this landscape, Directors underscored the
continued importance of employing a range of policy
tools, in a comprehensive, consistent, and well-
communicated manner, to secure the recovery and
improve medium-term prospects. They recognized that
major central banks have made every effort to commu-
nicate their monetary normalization policies to markets.
The cyclical upturn in economic activity provides a
window of opportunity to accelerate critical structural
reforms, increase resilience, and promote inclusiveness.
Directors stressed that a cooperative multilateral
framework remains vital for amplifying the mutual
benefits of national policies and minimizing any
cross-border spillovers. Common challenges include
maintaining the rules-based, open trading system;
preserving the resilience of the global financial system;
avoiding competitive races to the bottom in taxation
and financial regulation; and further strengthening the
global financial safety net. Multilateral cooperation is
also essential to tackle various noneconomic challenges,
among which are refugee flows, cyberthreats and, as
most Directors highlighted, mitigating and adapting
to climate change. Concerted effort is also needed to
reduce excess global imbalances, through a recalibra-
tion of policies with a view to achieving their domestic
objectives as well as strengthening prospects for strong,
sustainable, and balanced global growth. In this con-
text, as a few Directors emphasized, the IMF also has a
role to play by continuing to strengthen its multilateral
analysis of external imbalances and exchange rates.
Directors agreed that continued accommodative
monetary policy is still needed in countries with low
core inflation, consistent with central banks’ mandates.
Fiscal policy should gear toward long-term sustain-
ability, avoid procyclicality, and promote inclusive
growth. At the same time, fiscal policy should be as
growth friendly as possible, using space, where avail-
able, to support productivity and growth-enhancing
structural reforms. In many cases, policymakers should
prioritize rebuilding buffers, improving medium-term
debt dynamics, and enhancing resilience. Efforts to
raise potential output should be prioritized based on
country-specific circumstances, including increasing
the supply of labor, upgrading skills and human capi-
tal, investing in infrastructure, and lowering product
and labor market distortions. Social safety nets remain
important to protect those adversely affected by tech-
nological progress and other structural transformation.
Directors noted that income disparities among
countries have narrowed, but inequality has increased
in some economies. They saw a role that well-designed
fiscal policies can play in achieving redistributive
IMF EXECUTIVE BOARD DISCUSSION SUMMARY
The following remarks were made by the Chair at the conclusion of the Executive Board’s discussion of the
Fiscal Monitor, Global Financial Stability Report, and World Economic Outlook on September 21, 2017.

I M F e x e C u t I v e B o a r d d I s C u s s I o n s u M M a r y
International Monetary Fund | October 2017 xv
objectives without necessarily undermining growth and
incentives to work. Directors generally concurred that
there may be scope for strengthening means-testing
of transfers in many countries and for increasing the
progressivity of taxation in some others. Most Direc-
tors noted that any consideration of a universal basic
income would have to be weighed carefully against a
host of country-specific factors—including existing
social safety schemes, financing modalities, fiscal cost,
and social preferences, as well as its impact on incen-
tives to work—which, in the view of many Directors,
raised questions about its attractiveness and practical-
ity. Directors emphasized that improving education
and health care is key to reducing inequality and
enhancing social mobility over time.
Directors underlined the continued need for emerg-
ing market and developing economies to bolster
economic and financial resilience to external shocks,
including through enhanced macroprudential policy
frameworks and exchange rate flexibility. They noted
that a common challenge across these economies is how
to speed up their convergence toward living standards in
advanced economies. While priorities differ across coun-
tries, many need to improve governance, infrastructure,
education, and access to health care. In several countries,
policies should also facilitate greater labor force partici-
pation, reduce barriers to entry into product markets,
and enhance the efficiency of credit allocation.
Directors observed that the global financial system
continues to strengthen, and market confidence has
improved generally. They recognized the substan-
tial progress made in resolving weak banks in many
advanced economies, while a majority of systemic
institutions are adjusting business models and restoring
profitability. However, a prolonged period of monetary
accommodation could lead to further increases in asset
valuations and a buildup of leverage in the nonfi-
nancial sector that could signal higher risks to finan-
cial stability. These developments call for continued
vigilance about household debt ratios and investors’
exposure to market and credit risks. In this context,
Directors stressed the need to calibrate the path of nor-
malization of monetary policies carefully, implement
macro- and microprudential measures as needed, and
address remaining legacy problems.
Directors noted a generally subdued outlook for
commodity prices. They encouraged low-income
developing countries that are commodity export-
ers to continue improving revenue mobilization and
strengthening debt management, while safeguarding
social outlays and capital expenditures. Countries with
more diversified export bases should further strengthen
fiscal positions and foreign exchange buffers. Across all
low-income developing countries, an overarching chal-
lenge is to maintain progress toward their Sustainable
Development Goals.

Blank

Financial Stability Overview
Near-term financial stability risks have declined with the
strengthening global recovery, but medium-term vulnera-
bilities are building as the search for yield intensifies. Risks
are rotating from banks to financial markets as spreads and
volatility compress while private sector indebtedness rises.
The Global Recovery Is Improving the Near-Term Outlook
for Financial Stability
Near-term risks to financial stability continue to
decline. Macroeconomic risks are lower (Figures 1.1 and
1.2) amid the global upswing in economic activ-
ity, discussed in the October 2017 World Economic
Outlook (WEO). Emerging market risks have also
declined, underpinned by the pickup in global activity
and benign external conditions. This environment of
benign macroeconomic conditions and continued easy
monetary and financial conditions—but still sluggish
inflation—is fueling a marked increase in risk appetite,
broadening investors’ search for yield.
Systemically Important Banks and Insurers Continue to
Enhance Resilience
Global systemically important banks (GSIBs) and
insurers have strengthened their balance sheets by
raising capital and liquidity but are still grappling with
remaining legacy issues and business model challenges.
Prepared by staff from the Monetary and Capital Markets
Department (in consultation with other departments): Peter Dattels
(Deputy Director), Matthew Jones (Division Chief ), Paul Hiebert
(Advisor), Ali Al-Eyd (Deputy Division Chief ), Will Kerry (Deputy
Division Chief ), Zohair Alam, Sergei Antoshin, Magally Bernal,
Luis Brandão-Marques, Jeroen Brinkhoff, John Caparusso, Sally
Chen, Shiyuan Chen, Yingyuan Chen, Charles Cohen, Fabio
Cortes, Dimitris Drakopoulos, Kelly Eckhold, Martin Edmonds,
Jesse Eiseman, Jennifer Elliott, Caio Ferreira, Tamas Gaidosch,
Rohit Goel, Hideo Hashimoto, Sanjay Hazarika, Geoffrey Heenan,
Dyna Heng, Henry Hoyle, Nigel Jenkinson, David Jones, Jad
Khallouf, Robin Koepke, Tak Yan Daniel Law, Yang Li, Peter
Lindner, Rebecca McCaughrin, Aditya Narain, Machiko Narita,
Vladimir Pillonca, Thomas Piontek, Mamoon Saeed, Luca
Sanfilippo, Jochen Schmittmann, Juan Solé, Ilan Solot, Yasushi
Sugayama, Narayan Suryakumar, Francis Vitek, Jeffrey Williams, and
Christopher Wilson.
After a painful period of restructuring and absorption
of elevated charges for past misconduct in the form of
fines and private litigation, the outlook for sustainable
profitability is improving, but strategic reorientation
remains incomplete. The next section assesses risks
from large global banks and life insurance companies.
Medium-Term Vulnerabilities Are Rising and
Rotating to Nonbanks
Many asset valuations have continued to rise in
response to the improved economic outlook and the
search for yield (Figure 1.3, panel 1), driving down a
broad range of risk premiums (Figure 1.3, panel 2).
While increased risk appetite and the search for yield
are a welcome and intended consequence of unconven-
tional monetary policy measures, helping to support
the economic recovery, there are risks if these trends
extend too far. Compensation for inflation risks (term
premiums) and credit risks (for example, spreads on
corporate bonds) are close to historic lows, while
volatility across asset markets is now highly compressed
(Figure 1.3, panel 3). Some measures of equity valuation
are elevated, but relative to yields on safe assets (that
is, the equity risk premium) they do not appear overly
stretched. This prolonged search for yield has raised the
sensitivity of the financial system to market and liquidity
risks, keeping those risks elevated. The widening diver-
gence between economic and financial cycles within and
across the major economies is discussed in Box 1.1.
A key stability challenge is the rebalancing of central
bank and private sector portfolios against a backdrop
of monetary policy cycles that are not synchronized
across countries. Too quick an adjustment in monetary
policies could cause unwanted turbulence in financial
markets and set back progress toward inflation targets.
Too long a period of low interest rates could foster a
further buildup of market and credit risks and increase
medium-term vulnerabilities.
Credit risks are already elevated, given the deteri-
oration in underlying leverage in the nonfinancial
sector—households and firms—of many Group of
Twenty (G20) economies. Despite low interest rates,
IS GROWTH AT RISK?1CHAPTER
1International Monetary Fund | October 2017

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International Monetary Fund | October 2017
private sector debt service ratios in many major econ-
omies have increased to high levels because of rising
debt. Weaker households and companies in several
countries have become more sensitive to financial and
economic conditions as a result.
The Global Recovery Could Be Derailed
Prolonged low volatility, further compression of
spreads, and rising asset prices could facilitate addi-
tional risk taking and raise vulnerabilities further.
Investors’ concern about debt sustainability could
eventually materialize and prompt a reappraisal of
risks. In such a downside scenario, a shock to individ-
ual credit and financial markets well within historical
norms could decompress risk premiums and reverber-
ate worldwide, as explored later in this chapter. This
could stall and reverse the normalization of monetary
policies and put growth at risk.
Large Systemic Banks and Insurers:
Adapting to the New Environment
The large internationally active banks at the core of the
financial system—so-called global systemically important
banks (GSIBs)—have become more resilient since the crisis,
with stronger capital and liquidity. Banks have made sub-
stantial progress in addressing legacy issues and restructuring
challenges—while adapting their business models to the
new regulatory and market landscape. Strategic reorien-
tation has led to a pullback from market-related business.
Banks have, however, retained a presence in international
business and cross-border loans. These strategic realignments
have come amid changing group structures, as activity
is increasingly channeled through subsidiaries. Despite
ongoing improvement, progress is uneven and adaptation
remains incomplete. About a third of banks by assets may
struggle to achieve sustainable profitability, underscoring
ongoing challenges and medium-term vulnerabilities.
Life insurers were hit by the global financial crisis, but
have since rebuilt their capital buffers. However, they are
now facing the challenge of a low-interest-rate environment.
In response, insurers have adapted their business models
by changing their product mix and asset allocations. But
in doing so, they have been increasingly forced out of their
natural risk habitat in a search for yield, making them
more vulnerable to market and credit risks. Investors still
worry about the viability of some insurers’ business models
and find it difficult to assess risks, resulting in weak equity
market valuations. Policymakers should seek to strengthen
regulatory frameworks and increase reporting transparency.
Global Systemically Important Banks
Global banks remain critical pillars of international
financial intermediation. These GSIBs provide a wide
range of financial services for companies, institutions,
Global financial crisis
Source: IMF staff estimates.
Note: The shaded region shows the global financial crisis as reflected in the stability map of the April 2009 Global Financial Stability Report (GFSR).
Away from center signifies higher risks,
easier monetary and financial conditions,
or higher risk appetite.
Emerging market risks Credit risks
Market and liquidity risks
Risk appetiteMonetary and financial
Macroeconomic risks
Risks
Conditions
Figure 1.1. Global Financial Stability Map: Risks and Conditions
April 2017 GFSR
October 2017 GFSR
Risk appetite has grown markedly as near-term stability risks have declined.

3
C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
–1
0
2
1
–2
–1
0
–1
1
0
–1
0
1
0
1
Higher risk
–2
–1
0
1
2
3
Easier
Tighter
Higher risk appetite
Lower risk
Lower risk
Lower risk
Lower risk
Higher risk
Overall
(8)
Overall
(10)
Domestic
fundamentals
(4)
Volatility
(2)
Corporate
financing
(2)
External
financing
(2)
Macroeconomic
conditions
(4)
Uncertainty
(2)
Inflation or
deflation risks
(1)
Sovereign
risks
(1)
Overall
(3)
Asset allocation
preferences
(1)
Excess returns
(1)
Flows to risky
assets
(1)
Overall
(11)
Banking
sector
(4)
Household
sector
(3)
Corporate
sector
(4)
Overall
(12)
Liquidity and
funding
(5)
Volatility
(2)
Valuations
(3)
Position and
correlation risks
(2)
Figure 1.2. Global Financial Stability Map: Assessment of Risks and Conditions
(Notch changes since the April 2017 Global Financial Stability Report)
1. Macroeconomic risks have fallen, and macroeconomic
conditions have improved.
2. Emerging market risks are lower, driven by improved
fundamentals and external financing conditions.
3. Credit risks are unchanged, with improvements in the
banking sector contrasting with increasing corporate and
household sector risks.
4. Monetary and financial conditions remain accommodative,
as slightly higher real rates are offset by easier lending conditions
and financial conditions.
5. Risk appetite continues to increase, as reflected in robust
capital flows to emerging markets and increased performance and
allocations to risk assets.
6. Market and liquidity risks are unchanged, as compressed
risk premiums and low volatility offset less-extended market
positioning and improved trading liquidity conditions.
Source: IMF staff estimates.
Note: Changes in risks and conditions are based on a range of indicators, complemented by IMF staff judgment. See Annex 1.1 in the April 2010 Global Financial
Stability Report and Dattels and others 2010 for a description of the methodology underlying the global financial stability map. Overall notch changes are the simple
average of notch changes in individual indicators. The number in parentheses next to each category on the x-axis indicates the number of individual indicators
within each subcategory of risks and conditions. For lending conditions, positive values represent a slower pace of tightening or faster easing.
Overall
(6)
Monetary
policy
conditions
(3)
Financial
conditions
index
(1)
Lending
conditions
(1)
Central bank
balance sheet
(1)
Higher risk
Unchanged
Macroeconomic risks
Unchanged
Unchanged

4
G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
1. Search for Yield
(Percentile rank)
The global search for yield has compressed risk premiums across some assets …
2. Cross-Asset Valuations
(Percentile rank)
3. Realized Volatility
(Percentile rank)
… while volatility remains near precrisis lows.
Sources: Bank of America Merrill Lynch; Bloomberg Finance L.P.; Dealogic; Haver Analytics; Organisation for Economic Co-operation and Development; Thomson
Reuters; and IMF staff estimates.
Note: The color shading is based on valuation quartiles. Red (dark green) denotes low (high) premiums, spreads, volatility, and issuance quality, as well as high (low)
issuance and house price to income. In panel 1, quality of issuance shows spreads per turn of leverage. Quantity of issuance is 12-month trailing gross issuance as
percent of the outstanding amount. In panel 2, CAPE is the trailing 12-month price-to-earnings ratio adjusted for inflation and the 10-year earnings cycle. Forward P/E
is the 12-month forward price-to-earnings ratio. Equity risk premiums are estimated using a three-stage dividend discount model on major stock indices. Term
premium estimates follow the methodology in Wright 2011. Corporate spreads are proxied using spreads per turn of leverage. For house-price-to-income ratio,
income is proxied using nominal GDP per capita. The percentile is calculated from 1990 for CAPE, forward P/E, equity risk premiums and term premiums, from 1999
for EM term premiums, from 2000 for house-price-to-income ratio, and from 2007 for corporate spreads. In panel 3, the heatmap shows the percentile of
three-month realized volatility since 2003 at a monthly frequency. CAPE = cyclically adjusted price-to-earnings ratio; DM = developed market; EM = emerging
market; FX = foreign exchange; Govt = government; P/E = price to earnings.
Figure 1.3. Search for Yield, Asset Valuations, and Volatility
United States 83 79 85 7 6 74
Germany 62 33 86 9 14 39
Japan 28 17 87 5 65 8
United Kingdom 85 60 96 8 8 92
Emerging Markets 25 58 84 19 5 44
CAPE Equity Risk
Premiums
Term
Premiums
(10-year)
Corporate
Spreads
House
Prices to
Income
Forward P/E
High yield
EM
High yield
EM
High yield
EM
Spreads
Quantity of
Issuance
Quality of
Issuance
12 13 1714 15 162006 07 08 09 10 11
Global
financial
crisis
European
debt crisis
Precrisis
buildup of
risks
Latest0605 1211 1716151413042003 10090807
Oil sell-off,
China growth
worries,
Brexit, US
election
Equity DM 10%
Equity EM 6%
Govt. Bond DM 14%
Credit DM 14%
Credit EM 5%
FX DM 22%
FX EM 35%
Commodities 16%

5
C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
and individuals across many countries.1 Together, these
30 banks hold more than $47 trillion in assets and
more than one-third of the total assets and loans of
thousands of banks globally. They have an even greater
role in certain key global financial functions: collec-
tively they comprise 70 percent or more of certain
international credit markets (for example, syndicated
trade finance), market services, and the international
financial infrastructure. GSIBs are central to the inter-
national financial system (Figure 1.4, panel 1).
All GSIBs share systemic importance. At the same
time, they are a diverse group, with differences in
business mix and geographic positions. The 30 GSIBs
encompass business models ranging from those that
are market focused to those that are consumer focused
and from highly specific transaction banking models to
all-embracing universal banks (Figure 1.4, panels 3 and
4). About half of GSIBs, by assets, are universal banks,
offering a mix of services. Unsurprisingly, most operate
on more than one continent. But almost a third of
these banks, by assets, are largely domestic businesses
(mostly in China and the United States).
GSIBs Are Undergoing Business Model Transitions
In the aftermath of the crisis. GSIBs have been
reorienting their business models in three overlapping
phases (Figure 1.4, panel 2). First, a process of legacy
cleanup has been ongoing for most banks. As these legacy
challenges recede, banks have entered a phase of strategic
reorientation, which continues to affect both their lines of
business and geographic scope. As banks have progressed
in these first two phases, the focus is shifting to resolution
regimes and the associated need to reconfigure interna-
tional group structures for some banks. These multiyear
adjustments—still ongoing—have been necessary to
1Global systemically important banks (GSIBs) are identified based on
size, interconnectedness, cross-jurisdictional activity, impact on financial
institution infrastructure (for example, the payments system), and
complexity (Basel Committee on Banking Supervision 2014). GSIBs
included in the analysis are based on the list published in November
2016, the latest available at the time of this report, and include the
following: China (4)—Agricultural Bank of China (ABC), Bank of
China (BOC), China Construction Bank (CCB), Industrial and Com-
mercial Bank of China (ICBC); Japan (3)—Mitsubishi UFJ Financial
Group (MUFG), Mizuho Financial Group (MFG), Sumitomo Mitsui
Financial Group (SMFG); Continental Europe (11)—Banco Santander
(SAN), BNP Paribas (BNP), Crédit Agricole (CA), Credit Suisse (CS),
Deutsche Bank (DB), Groupe BPCE (BPCE), ING Groep (ING),
Nordea Bank (NDA), Société Générale (SG), UBS Group (UBS), Uni-
credit Group (UCG); United Kingdom (4)—Barclays (BARC), HSBC
Holdings (HSBC), Royal Bank of Scotland (RBS), Standard Chartered
(STAN); United States (8)—Bank of America (BOA), Bank of New York
Mellon (BNY), Citigroup (C), Goldman Sachs (GS), JP Morgan Chase
(JPM), Morgan Stanley (MS), State Street (STT), Wells Fargo (WFC).
support resilience and achieve more sustainable profitabil-
ity in the new environment. Progress on these fronts has
been positive, but uneven, and challenges remain.
Global Banks Have Fortified Balance Sheets and
Continue to Address Crisis Legacies
The resilience of GSIBs has improved over the past
decade as they have adapted to enhanced prudential
standards. They have significantly strengthened their
balance sheets with an additional $1 trillion in capital
since 2009 while reducing assets. Adjusted capital ratios
(incorporating reserves against expected losses) have
in aggregate risen steadily since the undercapitalized
precrisis period (Figure 1.5, panel 1). GSIB liquidity has
also improved: loan-to-deposit ratios are down from the
elevated levels a decade ago, and reliance on short-term
wholesale funding has fallen (Figure 1.5, panel 2).
In tandem with higher capital and more liquidity,
GSIBs have also made significant progress in dealing
with legacy challenges from the 2008–09 financial
crisis and its aftermath.
• Banks have made progress in cleaning up legacy
assets, facilitated by carving out noncore portfo-
lios (mainly legacy impaired loans and bonds) for
aggressive disposal and runoff (Figure 1.5, panel
3). About two-thirds of GSIB noncore assets have
been disposed of; US GSIBs are the most advanced
in this process. In contrast, several European banks
continue to take high charges to provide for and
write off legacy bad debts.
• Second, charges for past misconduct in the form of
fines and private litigation have eased from a high
level. These charges totaled an estimated $220 billion
between 2011 and 2016, equivalent to 27 percent of
underlying net income for European banks over the
period and 19 percent for US banks. Although some
of these charges were the result of misbehavior in
personal financial services (insurance products in the
United Kingdom, consumer protection in the United
States, private banking tax evasion at the global level),
most stemmed from market businesses (US residen-
tial mortgage-backed securities, fixing of the London
interbank offered rate) and international transactions
(anti–money laundering measures) in which GSIBs
dominate. From a financial stability point of view,
the litigation charges should strengthen incentives for
more prudent future business practices.
Despite progress in disposing of legacy assets and
dealing with past misconduct, GSIBs continue to cope

6
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International Monetary Fund | October 2017
0
10
20
30
40
50
60
70
80
90
100
0 25 50 75 100
Bank loans
Bank assets
Total exposures
Level 3 assets
International loans
EM US$ project finance
EM US$ syndicated loans
Payments
Underwriting revenues
Derivatives
Equity revenue
FICC revenue
Wealth
managers
Consumer
banks
Transaction
banks
Investment
banks
Corporate
banks
Universal
banks
Business
models
$110 trillion
$79 trillion
$0.6 trillion
$26 trillion
$50 billion
$87 billion
$60 billion
$600 trillion
$48 billion
$2,500 trillion
$300 billion
$54 trillion
Global
market
Legacy Strategy Structure
Ø NPL cleanup
Ø Portfolio runoff
Ø Conduct charges
Ø Restructuring costs
Ø Line of business
adjustments
Ø Geographic scope
Ø Efficiency and
capabilities
Ø Subsidiarization
Ø Cross-border
funding
Receding Continuing Emerging
Sources: Bank financial statements; Bank for International Settlements; Basel Committee on Banking Supervision; Bloomberg Finance L.P.; Dealogic; Haver Analytics;
Office of Financial Research; S&P Capital IQ; SNL Financial; and IMF staff estimates.
Note: In panel 1, global market size for total exposures, level 3 assets, payments, and over-the-counter derivatives are calculated using the GSIB indicator metrics.
“Total exposure” is a proxy for banks’ total asset exposures, which includes total consolidated assets, derivatives exposures, and certain off-balance-sheet
exposures. This is the same as the denominator used for the Basel III ratio. EM US$ project finance includes syndicated loans only. GSIBs’ apparently low share of
international loans reflects the nearly pure domestic focus of the local category banks as shown in panel 3. In panel 1, global banking loans and assets are calculated
using a sample of 3,500+ banks. See footnote 1 in the text for an explanation of the abbreviations in panels 3 and 4. EM = emerging market; FICC = fixed income,
currencies, and commodities; GSIB = global systemically important bank; NPL = nonperforming loan.
Figure 1.4. Global Systemically Important Banks: Significance and Business Model Snapshot
1. GSIBs’ Global Market Share by Asset or Activity, 2016 (or latest)
(Percent; US dollars)
2. Bank Business Model Challenges
3. GSIB Business Models and Geographic Strategies
4. GSIBs: Revenue Mix by Line of Business, 2016
(Percent of revenue)
Global Regional Local
Universal Bank Balance of household and business services
C, JPM, HSBC, DB,
STAN, BNP, MUFG
CA
BOA, ABC,
CCB, ICBC
56
Corporate
Bank Lending to businesses BARC, SMFG UCG, MFG
12
Investment
Bank
Capital markets services, advisory, mergers, and
secondary market sales and trading
GS, CS 3
Transaction
Bank
Corporate transaction services (including payments) and
institutional services (settlement, clearing, custody)
BNY, STT 1
Consumer
Bank
Retail banking including lending (mortgages, credit
cards, other unsecured credit), savings products, and
retail payment services
ING, SAN, SG NDA, BOC,
RBS
BPCE, WFC 23
Wealth
Manager
Asset management, private banking, and insurance MS, UBS 4
52 18 31 100
Percent of
GSIB Assets
Percent of GSIB Assets
Business
Model
Description
Geographic Reach
International credit
Market services
Market infrastructure
Overall balance sheet exposure
M
S
U
B
S
B
O
C
S
G
W
FC
N
D
A
R
B
S
B
PC
E
S
A
N
IN
G
S
TT
B
N
Y
S
M
FG
M
FG
B
A
R
C
U
C
G
G
S
C
S
IC
B
C
A
B
CC
M
U
FGD
B
C
C
B
B
O
A
S
TA
N
JP
M
B
N
P
H
S
B
C
C
A
Markets Corporate and investment banking Wealth management Consumer Transaction Commercial

7
C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
0
20
40
60
80
100
120
140
160
0
10
20
30
40
50
60
70
80
20
08 09 10 11 12 13 14 15 1
6
Europe excluding
United Kingdom
United States
China Japan
Total United Kingdom
Europe excluding
United Kingdom
United States
China Japan
Total United Kingdom
20
30
40
50
60
1
2
3
4
5
6
7
8
2005 06 07 08 09 10 11 12 13 14 15 16
Adjusted capital (trillions of US dollars, right scale)
Total assets (trillions of US dollars, left scale)
Adjusted capital to total assets (percent, right scale)
0
20
40
60
80
100
65
70
75
80
85
90
95
2005 06 07 08 09 10 11 12 13 14 15 16
Long-term securities and others (left scale)
Short-term borrowing and repos (left scale)
Deposits (left scale)
Loans to deposits (right scale)
0
50
100
150
200
300
0
50
100
150
200
250
250
20
08 09 10 11 12 13 14 15 16
0
500
1,000
1,500
2,000
0
5
10
15
20
20
09 10 11 12 13 14 15 16
Percent of assets (total)
Percent of assets (Europe)
Percent of assets (United States)
Ba
si
s
po
in
ts
(o
f e
qu
ity
)
Ba
si
s
po
in
ts
(o
f e
qu
ity
)
Cumulative conduct charges (right scale) Cumulative restructuring charges (right scale)
Bi
lli
on
s
of
U
S
do
lla
rs
Bi
lli
on
s
of
U
S
do
lla
rs
Bi
lli
on
s
of
U
S
do
lla
rs
Noncore assets (left scale)
Noncore Assets Litigation Expenses Restructuring Costs
Sources: Bank financial statements; Bloomberg Finance L.P.; Dealogic; S&P Capital IQ; SNL Financial; and IMF staff estimates and analysis.
Note: Adjusted Tier 1 capital equals shareholders’ equity, minus 45 percent (an estimate of average gross loss given default) of reported nonperforming loans, plus
loan-loss reserves. In panel 1, total assets are adjusted for the netted derivatives. In panel 3, conduct and restructuring charges (in basis points of equity) are on an
estimated posttax basis, assuming charges adjusted by effective tax rates.
Figure 1.5. Global Systemically Important Banks: Capital, Liquidity, and Legacy Challenges
1. Capitalization
Global banks are better capitalized …
2. Liquidity
(Percent)
… and hold higher liquidity …
3. Legacy Challenges: Noncore Assets, Litigation Expenses, and Restructuring Costs
… and have made good progress in addressing legacy challenges.

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G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
with restructuring charges. Most of these are severance
and other charges stemming from branch and staff
reductions motivated by banks’ efforts to reduce their
operating cost structures. Continental European and
UK banks are most affected; their restructuring charges
in 2016 amounted to $13 billion, equivalent to
25 percent of their underlying net income. Although
some GSIBs have made substantial progress in reduc-
ing staff, others (particularly some European GSIBs)
still report large restructuring charges.
Global Banks Have Reduced
Market-Related Business
Strategically, GSIBs have reduced their market-related
functions—investment banks have made some of the
biggest cutbacks (Figure 1.6, panel 1). This move came
as earlier overexpansion and excess capacity collided with
regulatory changes that increased risk-asset weight-
ing and capital charges and drove a sharp decline in
profitability of banks’ other lines of business (Figure 1.6,
panel 2). Fixed income, currency, and commodity
(FICC) businesses, in particular, have become less attrac-
tive to all but a few high-volume or high-margin players,
which have taken a greater share of a shrinking revenue
pie (Figure 1.6, panels 2 and 3). In this environment,
US banks have gained market share, and activity is now
concentrated in fewer players.
While GSIBs’ declining exposure to financial mar-
kets will reduce their risk, there may be associated costs
to market liquidity. Evidence that this change affects
market liquidity in normal times is mixed, and greater
participation by nonbank market intermediaries could
help address the fragmentation of market liquidity.
What is less clear is whether global banks’ reduced
capacity to intermediate in financial markets could
affect the resilience of liquidity in periods of stress.
Similarly, the supply of risk management services that
require GSIB balance sheet space and capital could
be reduced or provided to fewer clients. The balance
between reduced GSIB riskiness and potential costs to
liquidity during stress is an issue deserving of careful
ongoing consideration.2
2Work is underway at the Financial Stability Board, in collabo-
ration with standard-setting bodies, to evaluate the impact of the
regulatory reform agenda. But it will likely take some time to realize
the full impact of changes in bank business models on financial
activity. Adrian and others (2017) also document the stagnation of
broker-dealer balance sheets associated with deleveraging.
Global Banks Overall Continue to Operate
Internationally
In contrast to declining market intensity, GSIBs
overall have remained central to the provision of interna-
tional credit and services (including total loans and spe-
cific product markets, such as syndicated lending, trade
finance, and project finance). International balance sheet
commitments and revenue mix have remained quite sta-
ble across almost all GSIBs (Figure 1.7, panel 1). Even as
non-GSIB banks shrank international loans aggressively
during 2009–13 (owing to balance sheet pressures),
GSIBs as a group maintained their international lending
volume (Figure 1.7, panel 2).
Those GSIBs less impacted by the financial crisis have
maintained or expanded their international role. This
may in part be motivated by the relative profitability of
international operations. Across a sample of 724 banking
subsidiaries, foreign banking operations have been more
profitable than domestic business for Japanese and
continental European and UK GSIBs (Figure 1.7, panel
3). Japanese banks, whose international loans have con-
tributed to raising profitability, have continued to pivot
aggressively toward international markets—maintaining
their reliance on potentially volatile wholesale foreign
currency funding—accompanied by a general expansion
of corporate loans and foreign securities investments.
Shifts in international exposures of continental Euro-
pean and UK banks reflects three main crosscurrents.
A few—mainly UK banks—have emphatically cut
exposures in an international arena where they suffered
large losses. Some (mainly French) banks were forced by
balance sheet constraints to retrench. For many others,
international lending remains an attractive business to
which they have demonstrated commitment within the
constraints of their balance sheet capacity and expo-
sure limits.3 In contrast, US GSIBs, whose domestic
operations are highly profitable, have maintained or
slightly pulled back the international proportion of their
loan portfolios.
Subsidiarization Presents a Structural Challenge
for Some Banks
Largely in response to national regulatory pressures,
several GSIBs more reliant on branching have begun
gradually shifting their international lending from a
direct cross-border model to one based on lending via
3This could suggest that reduced international exposure may be
more a cyclical than a structural phenomenon for GSIBs, as sug-
gested for the broader banking sector by McCauley and others 2017.
See also Caruana 2017.

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C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
JPM
BOA
GS
MS
CITI
HSBC
BARC
STAN
United States
(47%)
Continental
Europe (27%)
RBS
DB
BNP
CS
SG UBS
MUFG MFG Others
$132 bn
JPM
BOA
GS
MS
CITIHSBC
BARC
STAN
RBS
DB
BNP
CS
SG UBS
MUFG
MFG Others
$87 bn
United Kingdom
(23%)
Others (3%)
United
States (52%)
Continental
Europe (23%)
United
Kingdom (17%)
Others (8%)
3. FICC Trading Revenues, 2010 and 2016
FICC revenue pool has shrunk with a shift in market share toward US
banks.
0 20 40 60 80 100
China
2010 2016
Japan Continental
Europe
United
Kingdom
United
States
Sources: Bank financial statements; Basel Committee for Banking Supervision; Bloomberg Finance L.P.; equity research reports; European Central Bank; Federal
Reserve Board; S&P Capital IQ; SNL Financial; and IMF staff estimates and analysis.
Note: In panel 1, market intensity is an index scaled (1 to 100) of relative exposures across the 30 GSIBs over 2010 to 2016. Each exposure is based on an average
of (1) market-risk-weighted assets divided by total risk-weighted assets; (2) Level 3 assets divided by total assets; (3) notional derivatives relative to total assets;
and (4) average value at risk relative to risk-weighted assets. In panel 2, business type is identified for each subsidiary entity based on a sample of 934 foreign and
domestic subsidiaries of the 30 GSIBs. Banking (724 subsidiaries) includes corporate, commercial, and consumer banking, and the advisory part of investment
banking. Markets (156 subsidiaries) include underwriting, secondary market trading in securities, currencies and commodities, and dealings in derivative contracts.
Wealth management (46 subsidiaries) includes asset management, private banking, and insurance. See footnote 1 in the text for an explanation of the abbreviations
in panels 1 and 3. FICC = fixed income, currencies, and commodities.
Figure 1.6. Global Systemically Important Banks: Market Activity
1. Market Intensity, 2010 and 2016
(Index, maximum intensity = 100)
Market intensity has declined sharply …
2. GSIBs by Home Region: Average Return on Assets, by
Business Type, 2014–16 Average
(Percent)
… as banks avoid relatively unprofitable markets businesses.
–0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Markets Banking Wealth management
9.5
CCB
ABC
BOC
ICBC
SMFG
MUFG
MFG
HSBC
STAN
BARC
RBS
ING
CA
UCG
BPCE
SG
SAN
BNP
NDA
DB
CS
UBS
BNY
WFC
STT
BOA
CITI
JPM
MS
GS
2016 2010
United
States
Continental
Europe
United
Kingdom
Japan
China

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International Monetary Fund | October 2017
0.0
0.3
0.6
0.9
1.2
1.5
0 20 40 60 80 100
CCB
ABC
BOC
ICBC
SMFG
MUFG
MFG
HSBC
STAN
BARC
RBS
ING
CA
UCG
BPCE
SG
SAN
BNP
NDA
DB
CS
UBS
BNY
WFC
STT
BOA
CITI
JPM
MS
GS
2010 2016
0
5
10
15
20
25
30
35
2006 07 08 09 10 11 12 13 14 15 16
20
30
40
50
60
70
80
China Japan Continental
Europe
United
Kingdom
United
States
China Japan Continental
Europe
United
Kingdom
United
States
From GSIBs From non-GSIBs
2011–13 averages 2014–16 averages
Domestic Foreign
Sources: Bank financial statements; Basel Committee for Banking Supervision; Bloomberg Finance L.P.; European Central Bank; Federal Reserve Board; S&P Capital
IQ; SNL Financial; and IMF staff estimates and analysis.
Note: Degree of internationality is an index scaled (1 to 100) of relative exposures across the 30 GSIBs over 2010 to 2016. Each exposure is based on an average of
(1) percent of revenue from nonhome regions; (2) international loans divided by total loans (or international assets divided by total assets); and (3) foreign deposits
divided by total deposits. For panel 2, see notes in Figure 1.6 for sample descriptions. In panel 3, subsidiary return on assets are based on reported earnings. The
reported earnings of subsidiaries in the United Kingdom and the United States may be understated due to the booking of conduct charges in those jurisdictions. See
footnote 1 in the text for an explanation of the abbreviations in panel 1. GSIBs = global systemically important banks.
Figure 1.7. Global Systemically Important Banks’ International Activity
1. Degree of Internationality, 2010 and 2016
(Index, maxiumum degree = 100)
GSIBs’ international activity has remained stable overall.
2. International Loans
(Trillions of US dollars)
GSIBs are increasing their share in international lending despite an
overall reduction.
3. GSIBs by Home Region: Average Return on Assets, Domestic
and Foreign Banking Subsidiaries, 2014–16 Average
(Percent)
Foreign banking operations are more profitable than domestic entities
for many banks.
4. GSIBs by Home Region: Overseas Subsidiaries’ Deposits as
Percent of Total Liabilities, 2011–13 and 2014–16 Averages
(Percent)
Subsidiaries of European and US GSIBs have increased their funding
through local deposits.
United
States
Continental
Europe
United
Kingdom
Japan
China

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C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
foreign subsidiaries (“subsidiarization”). The aggre-
gate share of GSIB lending extended through foreign
subsidiaries has risen from 40 percent to 60 percent
of international lending since 2009 and may continue
to increase gradually as banks respond to regulatory
pressure to house their activities in each international
jurisdiction within local legal entities with adequate local
capital and liquidity. This has motivated banks to shift
funding from cross-border (interbank and intragroup)
funding toward local deposits (Figure 1.7, panel 4).
These structural adjustments have helped improve
the resolvability and funding resilience of large, highly
interconnected global banks, which strengthens financial
stability. Healthy subsidiaries may also be better able to
withstand pressure on their parents or other affiliates,
which may have a positive effect on the stability of host
countries. These considerable benefits come with some
possible unintended costs. Keeping individual pools of
capital in subsidiaries across a group may lower returns
on equity as banks maintain higher levels of capital than
before subsidiarization. Lower mobility of capital and
liquidity might also compromise GSIBs’ capacity to
respond to solvency or liquidity shocks.4 This may be
more significant for banks that have a globally inte-
grated capital and liquidity model (most investment
banks) than for consumer banks. Moreover, regulatory
impediments to the flow of liquidity, risk management,
and funds deployment within the euro area contribute
to higher costs and reduced activity, adding to business
model and economic challenges. Again, officials will
need to consider the balance of costs and benefits of
these structural adjustments.
Progress toward Sustainable Profitability Is Uneven
Uneven progress in tackling legacy charges, business
model adaptations, and group structure has led to varied
profitability, as well as a mixed outlook across GSIBs
(Figure 1.8, panel 1). In part, this owes to the vigor
and timeliness in addressing legacy and capital chal-
lenges from the global financial crisis. Responding early
has paid off. US bank profitability, for example, has
reached levels in line with or exceeding 8 percent cost
of equity, a conservative estimate of investors’ required
returns, and approach management-stated targets for
their returns. European banks’ 2016 profitability, in
contrast, was more mixed, with several banks generating
4Chapter 2 of the April 2015 Global Financial Stability Report
(GFSR) discusses these issues further; see also Cetorelli and Goldberg
2012; Reinhardt and Riddiough 2015; and Fiechter and others 2011.
low returns, in part because of their slower progress in
addressing legacy issues. Overall, about half of GSIBs by
asset size remain below an 8 percent return on equity.
The outlook for sustained profitability is becom-
ing more favorable as legacy issues are more fully
addressed, business model improvements are imple-
mented, and the global recovery strengthens.5
Following a period of strong cyclical and structural
profitability headwinds over the past five years, prof-
itability drivers are turning up (Figure 1.8, panel 2).
After restructuring, weak and challenged banks’ assets
are set to increase again. This is expected to arrest
their revenue declines and to improve their reported
cost-ratio dynamics. Along with an expected cyclical
improvement in net interest margins, these develop-
ments should help increase return on assets.
However, even with these improvements and better
outlook, analysts expect one-third of the GSIB assets
(about $17 trillion) to generate below-sustainable returns
in 2019 (Figure 1.8, panel 3). For these banks, profitabil-
ity has been restrained by structural forces such as high
operating costs, low operating efficiency, and highly com-
petitive home markets, exacerbated in several cases by
weak information technology systems. Banks that exhibit
both thin capital buffers relative to future regulatory
requirements and relatively weak profitability to build
those buffers over the next few years warrant heightened
attention (see Figure 1.8, panel 4). Some banks continue
to grapple with legacy issues, while others, particularly
European investment banks, still face the fundamental
problem of defining and executing profitable business
models. An environment of low domestic interest rates
also affects the profitability of Japanese GSIBs. These
banks seek continued international expansion to offset
compressed domestic profitability, and supervisors must
bear in mind that such expansion increases currency and
maturity mismatch risks (see IMF 2017d). Problems
in even a single GSIB could generate systemic stress, so
supervisory action clearly needs to remain focused on
business model risks and sustainable profitability.
5This report defines banks as “weak” if they are expected to gen-
erate return on equity below 8 percent in 2019, “challenged” if the
expectation is between 8 and 10 percent, and “healthy” if more than
10 percent is expected. Investor surveys, cited in the October 2016
GFSR, suggest that the cost of equity is at least 8 percent. The current
cost of equity—inferred from current market prices using a Gordon
Growth model—is almost 11 percent for GSIBs as a whole; individual
bank estimates for the cost of equity range from 8 to 15 percent. Bank
management medium-term profitability targets are consistent with this
view: the target for 11 out of 21 GSIBs is a return on equity above
10 percent; for the remaining 10 banks, it is between 8 and 10 percent.

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International Monetary Fund | October 2017
0.0 0.2 0.4–0.3 –0.2 –0.2–0.1 0.0
ST
T
W
FC
JP
M G
S
M
S
B
N
Y
B
O
A C
N
D
A
U
B
S
IN
G
B
N
P
C
S
SA
N C
A
B
PC
E
SG
U
C
G
D
B
H
SB
C
R
B
S
B
AR
C
ST
AN
SM
FG
M
U
FG
M
FG
C
C
B
AB
C
IC
B
C
B
O
C
–10 –5 0 –3 2 7–0.4 –0.2 0.0 0.2 0.4–7 0 7
Healthy
Challenged
Weak
0
20
40
60
80
100
Asset growth
Healthy (ROE ≥ 10) Challenged (8 ≤ ROE < 10) Weak (ROE < 8) 2011–16 2016–19 Revenue/assets Cost/assets Return on assets Leverage Return on equity 4 5 6 7 8 9 10 11 12 14 16 13 15 10 12 14 16 18 20 Pr ofi ta bi lit y (R O E) CET 1 ratio United States Continental Europe United Kingdom Japan China Sources: Bank financial filings; Bloomberg Finance L.P.; SNL Financial; and IMF staff analysis. Note: Underlying profit is reported net income excluding conduct and litigation charges, restructuring costs, and noncash valuation adjustments. In panel 1, CS has an ROE of –0.3 percent in 2016. Management’s ROE targets, where not available directly, are estimated from their stated return on tangible equity targets, assuming a constant ratio of current tangible equity to total equity. In panel 2, asset growth is on an annualized basis. In panels 2 and 3, future asset forecasts are estimated using consensus RWA forecasts and assuming constant RWA density. In panel 3, a balanced sample of the current 30 GSIBs are considered for the entire duration. In all panels, 2016 numbers are used for BPCE due to lack of analyst forecasts. Forward-looking analyst forecasts consensus is gathered from Bloomberg. In panel 4, the colors correspond to those in panel 1. See footnote 1 in the text for an explanation of the abbreviations in panels 1 and 4. CET 1 = common equity Tier 1 capital; GSIB = global systemically important bank; ROE = return on equity; RWA = risk-weighted asset. Figure 1.8. Global Systemically Important Banks: Financial Performance Gaps 1. GSIB Return on Equity: 2016 Underlying, 2019 Consensus Forecasts, and Management Medium-Term Target (Percent) Most US GSIBs should reach profitability targets, but European and Japanese GSIBs face significant gaps. 2. GSIBs: Annualized Asset Growth in Percent and Changes in Profitability Drivers and Metrics (Percentage points) Balance sheet reflation and cost improvement are expected to help profitability ... 3. Percent of GSIB Assets by Return-on-Equity Thresholds, 2019 Consensus Forecasts ... whereas global banks, representing about one-third of GSIB assets, are still expected to have weak profits. 4. GSIBs: Profitability and Capital Position, 2019 Consensus Forecasts (Percent) Some banks have thin capital buffers and weaker profitability prospects. 2000–03 2004–07 2008–11 2012–16 2019E Target 494844 39 90 72 37 23 16 10 3 9 15 29 39 51 7 19 0 4 8 12 16 20162016201620162016 Medium-term target 2019 forecast 8 percent cost of equity WFC STT BNYMSGS C BOA JPM STAN RBSBARC HSBC CS UBS NDA SAN UCG DB BPCESG CA BNP ING SMFGMFG MUFG ABC BOC CCB ICBC 13 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Further Policies Are Needed Regulation and supervision of global systemically important banks have been considerably tightened in recent years, with detailed frameworks governing capital and liquidity and much more vigorous and regular monitoring. There has been less progress in making a resolution framework for international banks operational. Challenges include the need for further strengthening national resolution regimes, the development of cross- border resolution plans with adequate loss-absorbing capacity to make them effective, and close coordination between home and host-country regulators and resolution authorities, providing sufficient comfort for host coun- tries that a centralized resolution strategy would protect their interests. Only with such a framework in place will it be possible to avoid the potential negative consequences that can flow from the imposition of capital and liquidity requirements for GSIBs on a market-by-market basis. In addition, regulators should have a strong focus on risks from weak business models to ensure that weaker banks are able to achieve sustainable profitability. As dis- cussed in previous GFSR reports, this applies beyond the global banks that are the focus here. In particular, although euro area banks have made further progress in cleaning up their balance sheets, nonperforming loan ratios remain high in some countries, and profitability is still a challenge. Without a more concerted effort to reduce nonperforming assets and improve business models, financial stability con- cerns could be reignited in the euro area. More generally, continued progress toward completing banking union remains essential to strengthening the financial stability foundations of the euro area banking sector. Finally, it will be important to finalize Basel III to further strengthen the financial sector and create a more level international playing field. At a minimum, any proposals by national regulators to substantially ease capital, liquidity, or prudential standards should be considered carefully in light of their potential to damage the agenda of global regulatory harmonization. Insurers Life Insurers Have Rebuilt Capital Buffers since the Crisis Life insurers were hit hard by the global financial crisis. Profits tumbled, particularly in the United States (Figure 1.9, panel 1), and capital buffers fell.6 6This analysis is based on a sample of more than 80 life insurers from Belgium, France, Germany, Italy, Japan, the Netherlands, Nor- way, Spain, Sweden, the United Kingdom, and the United States. The sample covers almost two-thirds of total assets of life insurers in Europe, Japan, and the United States. 0 4 8 12 16 2005 06 07 08 09 10 11 12 13 14 15 16 –4 –3 –2 –1 0 1 2 Continental Europe Japan United States 2005–07 2008 2009–16 Global financial crisis Sources: Bloomberg Finance L.P.; and IMF staff estimates. Note: In panel 1, return on assets is calculated by dividing net income by total tangible assets minus separate accounts. In panel 2, the shareholder equity ratio is calculated by dividing the sum of common equity plus retained earnings by tangible assets minus separate accounts. In both panels, for Japan, separate accounts were not deducted in the denominator due to lack of data. Figure 1.9. Life Insurance Companies’ Profitability and Capital Amid falling yields and bullish asset markets, life insurers have managed to restore profits ... 1. Life Insurers: Return on Assets (Asset-weighted indices, period averages) ... allowing them to retain earnings and lift capital buffers. 2. Life Insurers: Shareholder Equity Ratio (Percent) Continental Europe JapanUnited States 14 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 But insurers have been able to build capital since then (Figure 1.9, panel 2). Bullish equity and bond markets have raised the value of the portion of insurers’ assets that are marked to market, helping boost earnings, dividend payouts, and capital. Life Insurers Have Been Adapting Their Business Models to Cope with Historically Low Returns While building capital levels, life insurance compa- nies have also been adapting their business models in response to the low-yield environment. Several changes have been made in the face of lower investment spreads. First, insurers have reduced the guaranteed returns on new policies (Figure 1.10, panel 1). Second, they have adjusted their product mix (Figure 1.10, panel 2). Euro- pean insurers have gradually sold more unit-linked poli- cies. These policies sell units similar to those in a mutual fund and shift market risk to policyholders. US insurers have moved from variable to fixed annuities, which are easier to hedge. Japanese insurers have favored the sale of 0 20 40 60 80 100 2008 16 54 33 24 18 67 75 0 20 40 60 80 100 2008 16 811 42 23 4 65 83 AAA/AA/A BBB Not IG NR and other 2 3 4 5 6 2006 07 08 09 10 11 12 13 14 15 16 2009 15 100 bps 50 bps 25 bps 2009 16 0 20 40 60 80 100 2007 15 Europe: gross written premiums United States: sales Japan: gross written premiums Unit linked Nonlinked Fixed annuities Variable annuities Life insurance AnnuitiesGuaranteed returns Japan Investment returns Japan Guaranteed returns United States Investment returns United States Guaranteed returns Germany Investment returns Germany 0 20 40 60 80 100 2004 16 Cash, loans, real estate, and other Equities Foreign bonds Domestic bonds, maturity > 10
years
Domestic bonds,
maturity < 10 years United States Europe Figure 1.10. Changes in Life Insurance Companies’ Business Models 1. Average Investment Returns and Guaranteed Returns (Percent, on existing portfolios) Facing investment spread compression, life insurers in Germany, Japan, and the United States have reduced guaranteed returns ... 2. Changes in Insurance Product Mix (Percent) ... and have been gradually changing their product mix. 3. European and US Life Insurers: Bond Asset Allocation (Percent) Searching for yield, US and European life insurers have invested more in lower-rated bonds ... 4. Japanese Life Insurers’ Investment Portfolio (Percent) ... and Japanese life insurers have increased duration and holdings of foreign bonds. Sources: Bundesbank; NLI Research Institute; and Office of Financial Research. Note: bps = basis points. Sources: European Insurance and Occupational Pension Authority; Life Insurance Association of Japan; and Life Insurance and Market Research Association. Sources: SNL Financial; and IMF staff estimates. Note: Not IG = noninvestment grade: bonds with ratings lower than BBB–; NR = not rated. NR and other may include some loans. Source: Bank of Japan. 15 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 insurance products over saving products. However, these changes have been slow to affect balance sheets given the large amount of legacy policies that remain. In addition, insurers have been adjusting their asset mix to higher-yielding and less liquid assets, moving out of their natural investment habitat in search of yield. • Insurers have taken on more credit risk. Despite risk-sensitive capital requirements, at least one-third of US and European insurers’ bond portfolios now have a BBB rating or lower (Figure 1.10, panel 3).7 Additional risk taking has also been taking place in the United States—for example, using unregulated subsidiaries, which do not face the same capital requirements as insurers. • Insurers have taken on more market risk. Japanese and US insurers have extended the maturity of domestic bond holdings to better match the dura- tion of their liabilities and enhance yields. Over the past five years, portfolio durations in the United States have increased from about five to eight years overall. Japanese life insurers have also invested in higher-yielding foreign bonds, partly exposing them to currency risk (Figure 1.10, panel 4). • Insurers have taken on more liquidity risk. Exam- ples include commercial property, infrastructure financing, private placements, structured securities, and mortgage loans. In the United Kingdom, about 25 percent of annuities are currently backed by illiq- uid investments, and insurers have plans to increase that proportion to 40 percent by 2020.8 Market Concerns about Insurers Persist Despite these changes, insurers continue to face profitability pressure (Figure 1.11, panel 1), and investors remain concerned about life insurers’ business models, as reflected in market valuations. Half of the US and European insurers in the sample, by assets, now have a price-to-book ratio both below precrisis levels and below one (Figure 1.11, panel 2), reflecting concerns over future profitability in a low-rate environ- ment, as well as difficulties in assessing risks. • Profitability: Despite efforts to change business models, insurers in a significant group of countries continue to face both high guaranteed returns and 7Part of this change can be attributed to downgrades of bonds that were already in the bond portfolios of insurers. 8See Bank of England 2017. high duration mismatches (Figure 1.11, panel 3).9 If low interest rates persist, investment returns could continue to decrease for the next decade, a situation that would leave life insurers in the Netherlands, Germany, Sweden, and Norway facing negative spreads within a few years. Even if interest rates were to increase by 100 basis points, many insurers would still face this risk (Figure 1.11, panel 4). • Risk assessment: Investors continue to have difficul- ties adequately assessing risk in the sector because regulatory regimes are evolving and disclosure is inadequate. For example, discount rates used to value future liabilities differ between insurers and are often higher than market risk-free rates, result- ing in an underestimation of liabilities. Regulatory gaps (discussed later in this chapter) make it hard to compare risks in insurers across countries. Options embedded in some insurance contracts are also hard to value, making it difficult to assess balance sheet risks. Life Insurers Are More Vulnerable to Market and Credit Risks Business model adjustments on the asset side have made insurers more vulnerable to a decompression of risk premiums and falls in asset prices. A sharp decline in equity and real estate markets, combined with an increase in credit spreads and a flight to high-quality sovereign bonds, would amount to a double hit on insurers’ balance sheets in this scenario. Asset values would fall, while liabilities would increase as risk-free rates used to discount future liabilities decline. Fig- ure 1.12 shows a simulation of such a scenario, in which assets and liabilities are fully marked to market. However, current accounting and regulatory rules exempt insurers from marking all their liabilities to market and allow them to dampen market shocks through adjustments to liabilities. In the simulation, life insurers in Italy, Spain, and the United States would be affected by their lower-rated sovereign and corporate bond holdings. Insurers in Germany, the Netherlands, Norway, and Sweden would be affected by the relatively long duration of their liabilities. If such a shock were to occur, it could mean that life insurers would be unable to fulfill their role as financial intermediaries, precisely when other parts of the finan- 9See Chapter 2 of the April 2017 GFSR. 16 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 0 20 40 60 80 100 2005–07 2014–16 ROE < 8% 8% ≤ ROE ≤ 10% ROE > 10%
0
1
2
3
4
5
0 1 2 3 4 5
Europe
United States
Half of European
and US sample
–6
–4
–2
0
2
4
6
8
10
12
14
16
–3.5 –3.0 –2.5 –2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5
IRL
ITA
GBR
JPN
NOR
SWEDEU
NLD
KOR CHE
CAN
USA
FRA
BEL
Long-term yield on sovereign bonds minus average guaranteed returns
(percent)
D
ur
at
io
n
of
li
ab
ili
tie
s
m
in
us
du
ra
tio
n
of
a
ss
et
s
(y
ea
rs
)
20
17
(y
ea
r-
to
-d
at
e
av
er
ag
e)
Precrisis (2005–06 average)
Bank averages
2005–07 2014–16
United States Europe Japan
2005–07 2014–16
1. Life Insurers: Return on Equity
(Period average, percent of sector assets per category)
Legacy liabilities are a drag on their profitability …
2. Life Insurers’ Price-to-Book Ratios
… such that half of European and US insurers are valued below their
book values and below precrisis levels.
3. Duration Mismatch and Guaranteed Return Spreads
Guarantees and duration mismatches remain high for a large part of
the sector.
4. Projected Number of Years until Bond Yields Fall below
Guaranteed Returns
Some insurers may soon face negative investment spreads.
Sources: Annual reports; Autorité de Contrôle Prudentiel et de Résolution; Bloomberg Finance L.P.; Bundesbank; De Nederlandsche Bank; European Insurance and
Occupational Pensions Authority; Moody’s Investors Service; National Association of Insurance Commissioners; Nationale Bank van België; NLI Research Institute;
Office of Financial Research; Organisation for Economic Co-operation and Development; SNL Financial; and IMF staff estimates.
Note: In panel 1, the implied cost of capital was about 10 percent before and after the global financial crisis. In panel 3, the size of the bubble relates to the share of
liabilities with guaranteed returns to total life insurance liabilities. Green = countries with insurance sectors that have low guaranteed returns and low or negative
duration mismatch. Yellow = countries with insurance sectors that have either high guaranteed returns or a high duration mismatch. Red = countries with insurance
sectors that have both high guaranteed returns and high duration mismatch. In both cases in panel 4, guaranteed returns continue to decline. In the case of a 100
basis point increase in bond yields, Belgian, Japanese, and US investment yields are not expected to fall below guaranteed returns. Data labels in the figure use
International Organization for Standardization (ISO) country codes. ROE = return on equity.
Figure 1.11. Life Insurers’ Market Valuations and Risk Outlook
0
4
8
12
16
USA BEL JPN NLD DEU SWE NOR DEU NLD SWE NOR
Current interest rate
environment
100 basis point
increase in
sovereign and
corporate bond
yields

17
C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
cial system are also failing to do so.10 This highlights
the importance of guarding against complacency
and the need for additional policy focus on nonbank
financial institutions and financing markets and the
extension of macroprudential tools.
Policies Are Needed to Ensure Greater
Insurer Resilience
Life insurers face growing vulnerabilities in the
continued low-interest-rate environment. Policymakers
should ensure that as insurers adapt to this environ-
ment they do not take excessive risks. Risk assessment
in the insurance sector suffers from opaque and het-
erogeneous financial disclosure and deficiencies in the
accounting and regulatory regimes. Policymakers must
continue to strengthen regulatory frameworks and
increase reporting transparency.
Greater public disclosure of timely information on
key metrics to assess interest rate risk (namely, guaran-
teed returns and duration mismatches) would motivate
insurers to further adapt their business models and
build additional capital buffers. Liabilities are often
not valued using current market prices (Japan, United
States) or are understated by country- and firm-specific
adjustments (Europe), hampering comparability. In the
United States, there is no consolidated capital require-
ment, and sector-wide stress tests are not regularly
undertaken, which leaves the potential for firms to
mask risks. In Europe, the lack of loss-absorbing capac-
ity in some instruments eligible as regulatory capital
harms the credibility of reported solvency positions.
Regulators are encouraged to close these regulatory
gaps. In particular, the International Association of
Insurance Supervisors should accelerate its efforts to
establish a global insurance capital standard that ade-
quately addresses these underlying vulnerabilities.
Monetary Policy Normalization: A Two-Sided Risk
Central bank balance sheets have grown considerably due
to large-scale asset purchase programs. This has forced
substantial portfolio adjustments in the private sector
and across borders, reducing government bond yields,
term premiums, and credit spreads while boosting equity
valuations. As the global recovery progresses, a key stability
challenge is to gradually rebalance central bank and
private sector portfolios against the backdrop of monetary
policy cycles that are not synchronized across countries.
10See also Chapter 3 of the April 2016 GFSR.
Too quick an adjustment could cause unwanted turbu-
lence in financial markets and international spillovers.
However, the expected process of normalization is likely
to be gradual, with continued easy monetary conditions
and low volatility that could foster a further buildup of
financial excesses and medium-term vulnerabilities.
Managing the gradual normalization of monetary pol-
icies presents a delicate balancing act. The pace of nor-
malization cannot be too fast or it will remove needed
support for sustained recovery and desired increases in
core inflation across major economies. The substantial
rebalancing of private portfolios that has occurred also
makes the adjustment of financial market prices much
less predictable than in previous cycles. On the other
hand, the likely prolonged period of low interest rates
could further deepen financial stability risks as investors
take on more risk in their search for yield.
–10
–8
–6
–4
–2
0
2
4
6
8
10
AUT BEL DEU ESP FRA GBR ITA JPN NLD NOR POR SWE USA
Changes in liabilities
Changes in assets
Sources: Bank of Japan; European Insurance and Occupational Pensions Authority;
Life Insurance Association of Japan; Moody’s Investors Service; National
Association of Insurance Commissioners; and IMF staff estimates.
Note: Cash flows are fixed. Derivative positions and loss absorption by policyhold-
ers and by taxes and regulatory adjustments are not taken into account. This
implies that results should be considered an upper-bound impact. Shocks are
applied to aggregate sector balance sheets of solo life insurers as of 2016:Q3
(Europe), 2016:Q1 (Japan), and 2015:Q4 (United States). The following shocks are
applied: equity (–10 percent); real estate (–6 percent); sovereign debt yield AAA–A
(–50 bps), BBB (+100 bps), < BBB (+100 bps); corporate bond yields AAA–A (+50 bps), BBB (+150 bps), < BBB (+200 bps); risk-free rates (–50 bps). Data labels in the figure use International Organization for Standardization (ISO) country codes. bps = basis points. Figure 1.12. Simulated Mark-to-Market Shocks to Assets and Liabilities (Percent) 18 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Uncertainty around Central Bank Balance Sheet Adjustments Large-scale asset purchase programs by the major central banks have led to a considerable shift in port- folios by domestic and foreign investors (Figure 1.13, panels 1 and 2). Central banks in Japan, the United Kingdom, the United States, and the euro area have increased their holdings of outstanding government securities to 37 percent of GDP, up from 10 percent before the global financial crisis. These purchases have produced marked shifts in asset allocations across major advanced economies during their respective periods of quantitative easing (QE). • The Bank of Japan’s QE program, the most aggres- sive of those of major advanced economy central banks, led domestic banks and pension funds to reduce their Japanese government bond holdings. The European Central Bank’s QE program also had a large impact in altering the composition of port- folios: foreigners significantly reduced their holdings of government debt, followed by domestic banks and pension funds. In the United States, the Federal Reserve’s QE programs led to a more muted shift: foreigners reduced their holdings of Treasuries as the accumulation of foreign exchange reserves slowed, as did insurance companies and pension funds, but other investors increased their holdings, including banks (to satisfy liquidity requirements), households, and mutual funds. The extent of the QE programs across central banks largely reflected the severity of the deflationary pressures experienced since the crisis began. • Some 100 percent or more of the supply of gov- ernment bonds has been absorbed by central bank purchases in the euro area and Japan. Official demand for Japanese government bonds exceeded net issuance in early 2013, while official purchases of euro area government debt eclipsed net issuance in 2016 as the growth in government deficits slowed (Figure 1.13, panel 3). But even though the Federal Reserve’s QE programs were large in absolute terms, they were more modest relative to net issuance, which explains their more muted impact on investor portfolio rebalancing.11 11Federal Reserve asset purchases accounted for a lower share of net issuance of US Treasuries, but a much greater share of quasi-agency mortgage-backed securities (net issuance in excess of 100 percent). • By reducing the stock of fixed income instruments available to the private sector, central banks crowded out traditional investors, such as banks, insurance companies, and asset managers, to differing degrees (Figure 1.13, panel 4). This prompted some private investors to reach for duration, credit, and liquidity risk to increase returns—an intended and beneficial consequence of asset purchase programs. Going forward, portfolio rebalancing will have an impact on term premiums and broader risk premiums through two main channels. First, by releasing partic- ular assets, central bank balance sheet normalization will increase their net supply to the public and may increase their term and risk premiums (the portfo- lio balance channel) (Figure 1.13, panel 4). Second, normalization will be associated and consistent with higher future short rates (the signaling channel). There is significant uncertainty as to the magni- tude of the adjustment in term premiums, given the unique set of conditions—large central bank bal- ance sheets, a prolonged period of accommodation, diverging monetary policy cycles, and uncertain effects of postcrisis reforms and portfolio substitution. The magnitude holds great import: sovereign bond yields are the benchmark rate for a wide range of other assets, and term premiums are an input for broader risk premiums. Historically, policy rates and term premiums have not always moved in unison; indeed, they diverge quite often (Figure 1.14, panel 1). Once the central bank starts increasing policy rates, it also provides forward guidance, reducing uncertainty (over interest rates and inflation). Consequently, bond risk and term premi- ums decline. Indeed, term premiums actually declined during the two most recent US tightening cycles; even previous monetary tightening cycles draw at best a mixed picture.12 But historical precedent may not be a helpful guide, given the large size of central bank balance sheets and compressed term premiums (Figure 1.14, panel 2). In the case of the United States, the Federal Reserve esti- mates that market expectations of a gradual unwinding and fall in the maturity of its securities holdings would increase the term premium by about 15 basis points by the end of 2017, at which point QE would still be holding down term premiums by a total of about 12Adrian, Crump, and Moench 2013. 19 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 –2 0 2 4 6 8 10 12 14 16 18 22 20 2010 11 12 13 14 15 16 17 18 19 20 21 22 0 20 40 60 80 100 2004 05 06 07 08 09 10 11 12 13 14 15 16 –500 0 500 1,000 1,500 2,000 2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 Euro area—private2 Japan—private United Kingdom—private United States—private G4—official3 0 5 10 15 20 25 30 35 40 –1,000 –500 0 500 1,000 1,500 2,000 2,500 3,000 Jan. 17Jan. 15Jan. 13Jan. 11Jan. 09Jan. 07Jan. 2005 Fed ECB BOJ BOE Total Stock Aggregate stock as a share of GDP Domestic central bank Foreign official sector Domestic bank Domestic nonbank Foreign bank Foreign nonbank Pr iv at e in ve st or s –500 0 500 1,000 1,500 2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 –200 0 200 400 600 800 2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 United States Euro Area Japan Net issuance Sovereign bond purchases Forecast 1. Change in Central Bank Balance Sheet Assets (Billions of US dollars, 12-month rolling sum, left scale; percent of GDP, right scale) Central bank balance sheets have expanded because of large-scale asset purchases ... 2. Advanced Economy Sovereign Bond Holdings by Investor Type (Percent) ... leading domestic and foreign central banks to capture a sizable share of sovereign debt. 3. Government Bond Issuance and Official Demand (Billions of US dollars, 12-month moving sum) Large official purchases have outstripped net issuance in the euro area and Japan ... 4. Change in Stock of Advanced Economy Sovereign Debt, by Region of Issuance and Holder1 (Trillions of US dollars, cumulative change since beginning of 2010) ... but going forward, the private sector will need to absorb additional supply. Sources: Bank of England; Bank of Japan; European Central Bank; Federal Reserve; government sources; Morgan Stanley; World Bank; Arslanalp and Tsuda 2012, updated; and IMF staff estimates. Note: Panels 2–4 exclude agency debt securities. In panel 4, debt stocks are converted to US dollars using end of quarter exchange rates; ECB net purchases are assumed to decline to a reduced pace and the asset purchase program extended to June 2018; Fed net purchases are assumed to follow the path outlined by the Fed starting in 2017:Q4; BOJ net purchases are assumed to equal forecast net supply; BOE net purchases are assumed to equal zero from 2017:Q1 onward. BOE = Bank of England; BOJ = Bank of Japan; ECB = European Central Bank; Fed = US Federal Reserve; G4 = euro area, Japan, United Kingdom, United States; QE = quantitative easing. 1Forecasts use forecasted central government net lending/borrowing. 2The following member countries of the euro area are included: Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, and Spain. 3Until end-2016, debt absorbed by central banks and foreign and supranational institutions; from 2017 onward, aggregated central bank purchases. Figure 1.13. Central Bank Balance Sheets and the Sovereign Sector 20 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 85 basis points, with the portfolio balance channels accounting for two-thirds of the impact.13 An inflation surprise on the upside could also lead to a sharp jump in term premiums. Potential International Spillovers Pose Additional Challenges and Risks Because of the different starting points and time paths for both economic recovery and the state of financial repair, the international aspects of balance 13Bonis, Ihrig, and Wei 2017. sheet normalization and spillovers are significant for two reasons: • The domestic effects of balance sheet normalization may be transmitted to other economies because global financial markets are highly integrated. Balance sheet normalization in major advanced economies could tighten financial conditions in other countries, raising long-term rates and induc- ing capital outflows from those countries. This is because term premiums exhibit a high degree of comovement, particularly if they originate from shocks from the largest global bond markets, –200 –100 0 100 200 300 400 500 600 700 JPN GBR CAN DEU USA Current Maximum Mean Minimum 0 10 20 30 40 50 60 80 70 t – 0 End-2017 End-2018 End-2019 End-2020 –1 0 1 3 2 4 5 6 2007 08 09 10 11 12 13 14 15 16 17 YTD Japan United Kingdom United States Euro area Average Maximum Minimum Date of minimum Jun. 16 Sep. 16Sep. 16 Jun. 16 Jun. 16 1994 cycle 1999 cycle 2004 cycle Current cycle Quarters after first rate hike –100 –50 0 50 100 150 200 250 300 350 400 0 1 2 3 4 5 6 7 8 9 10 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 Cumulative change in term premiums Cumulative change in policy rate Treasury 10-year yield (right scale) Ba si s po in ts Pe rc en t 1. Federal Funds Rate and Term Premiums during Previous Monetary Policy Cycles Policy rates and term premiums have diverged during recent monetary policy tightening cycles ... 2. Term Premiums in Advanced Economies (Basis points, 1990–2017) ... but term premiums are near historical lows in several major economies. 3. Market-Implied Cumulative Change in Policy Rates (Basis points) Monetary policy cycles are diverging ... 4. Overnight Indexed Swap Forward Rate Curves for Advanced Economies (Percent) ... and markets expect a slow pace of tightening. Sources: Bloomberg Finance L.P.; and IMF staff estimates based on Wright 2011. Notes: Panel 4 shows annual average three-month overnight indexed swap (OIS) rates on forward contracts for tenors from six months to five years. The OIS forward curves are constructed from the US dollar, euro, Japanese yen, and British pound, and the average, maximum, and minimum are computed for each tenor across the four jurisdictions. Data labels in the figure use International Organization for Standardization (ISO) country codes. YTD = year to date. Figure 1.14. Policy Rates, 10-Year Government Bond Yields, and Term Premiums 21 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 such as the United States, Germany, and Japan (see the October 2016 GFSR). These heightened cross-border dynamics could potentially trigger a large simultaneous increase in global rates. This poses challenges because of diverging monetary pol- icies (Figure 1.14, panel 3) and paths for normaliza- tion (Figure 1.14, panel 4). • Differences in balance sheet repair across countries could create additional sources of financial stress as monetary policy normalizes. For example, euro area sovereign term spreads could increase further as the prospect of reduced monetary accommoda- tion moves closer. Although this could partly reflect rising inflation expectations, it could also signal increased credit risks in countries with high debt burdens given the prospect of further reductions in European Central Bank (ECB) net asset purchases. How Will Emerging Market Economies Fare amid Reduced Central Bank Support? Large-scale monetary accommodation has under- pinned a significant portion of portfolio flows to emerging market economies. Model estimates indicate that about $260 billion in portfolio inflows since 2010 can be attributed to the push of unconventional poli- cies by the Federal Reserve (Figure 1.15, panel 1).14 These estimates suggest that the expected steady pace of Federal Reserve policy normalization over the next two years (as described in the baseline of the October 2017 WEO) could reduce portfolio flows by about $35 billion a year (Figure 1.15, panel 2). Countries that benefited the most during the boom period could see the largest moderation in inflows. If so, Chile, Mexico, and South Africa would be expected to experience the greatest decline in inflows 14Estimates for portfolio flows are obtained using a model adapted from Koepke 2014. The model estimates the impact of external “push” and domestic “pull” variables on portfolio flows to emerging markets, consistent with the capital flows literature. The dependent variable is monthly data from the Institute of International Finance on nonresident portfolio flows to emerging market economies (that is, foreign purchases of emerging market stocks and bonds). Inde- pendent variables include push factors, pull factors, and a constant term. Push variables include a proxy for global risk aversion (the US corporate BBB spread over Treasuries), three-year-ahead expectations for the federal funds effective rate, and the change in assets on the Federal Reserve’s balance sheet. Pull variables include an emerging market economic surprise index compiled by Citigroup and the Morgan Stanley Capital International Emerging Markets Index. The (positive) constant term captures the sizable passive component of portfolio flows, which is due to portfolio growth and passive reallo- cation (and thus unrelated to push or pull factors). relative to the size of their economies, estimated at a cumulative 1.0 to 1.5 percent of annual GDP over the next two years (Figure 1.15, panel 3). It is worth noting, however, that emerging market economies with previously large inflows are generally those with deeper and more liquid markets that are able to with- stand outflows better. Countries that have benefited the most from inflows owe some of this benefit to strong domestic factors, such as improving growth and external positions and declining corporate vulner- ability. To the extent that such favorable conditions are maintained, the impact of a less favorable external environment would be mitigated, including via other types of foreign capital inflows, such as foreign domestic investment. Emerging market economies should be able to handle this reduction in inflows in a relatively smooth manner, given their enhanced resilience and stronger growth outlook. However, a rapid increase in inves- tor risk aversion would have a more severe impact on portfolio inflows and prove more challenging, particularly for countries with greater dependence on external financing. For example, Malaysia, Poland, South Africa, and Turkey are projected to have sizable external financing needs through 2020 (Figure 1.15, panel 4). However, pressures from external shocks can be mitigated by large external asset holdings of domes- tic investors and banks. Monetary Policy Changes Should Be Well Communicated to Prevent Excessive Market Volatility The baseline path for the global economy foresees continued support from accommodative monetary policies, as inflation rates are expected to recover only slowly. Too quick an adjustment could cause unwanted turbulence in financial markets while removing needed support for the recovery. To ensure a smooth nor- malization of monetary policy, monetary authorities should provide and follow well-communicated plans on unwinding their holdings of securities and, if needed, provide guidance on prospective changes to the framework. At the same time, authorities need to be mindful of potential global spillovers as normaliza- tion proceeds. These efforts will help anchor market expectations and avoid undue market dislocations or excessive volatility. Central banks with still-expanding balance sheets will need to take appropriate measures to alleviate col- 22 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 lateral scarcity pressures in order to support liquidity resilience and efficient market functioning. • For the European Central Bank, subdued inflation points to the need for monetary policy to remain accommodative for an extended period.15 To this end, the ECB has committed to keeping policy rates at their current levels until well past the horizon of net asset purchases. It will be important to adhere to this commitment, thus ensuring the credibility of forward guidance and maintaining accommodation even if supply constraints neces- 15See IMF 2017c. sitate scaling back net asset purchases next year. Moreover, reinvesting the proceeds from maturing assets would keep the central bank balance sheet from shrinking. • For the Bank of Japan, stubbornly low inflation underscores the importance of maintaining sus- tained accommodation through its “quantitative and qualitative easing with yield curve control” framework.16 The Bank of Japan should carefully calibrate its yield curve policy in the event of downside risks, including by considering lowering 16See IMF 2017d and IMF 2017e. –1.0 –0.5 0.0 Oct. 2017 Apr. 18 Oct. 18 Apr. 19 Oct. 19 –10 0 10 20 30 40 MYS TUR POL ZAF CHL MEX IND COL BRA CHN IDN RUS –100 0 100 200 300 400 2010 12 14 16 –80 –60 –40 –20 0 –1,350 –1,200 –1,050 –900 –750 –600 –450 –300 –150 0 Oct. 2017 Apr. 18 Oct. 18 Apr. 19 Oct. 19 Flows impact of Fed balance sheet reduction Flows impact of Fed policy expectations Fed balance sheet reduction (right scale) –1.5 Portfolio balance (Fed QE) Fed policy expectations Global risk appetite EM domestic factors China India Brazil Indonesia Turkey Poland Colombia Malaysia Chile Mexico South Africa Short-term debt on remaining maturity basis (2018–20 average) Current account deficit (2018–20 average) External financing requirement (2018–20 average) 15 percent external financing requirement threshold 1. Model Estimates: Cumulative Contributions to Emerging Market Portfolio Flows (Billions of US dollars) A large portion of portfolio flows has been driven by US monetary policy accommodation. 2. Estimated Cumulative Monthly Contributions to Emerging Market Portfolio Flows, 2017–19 (Billions of US dollars) Estimates point to a substantial reduction in portfolio flows due to US monetary policy normalization ... 3. Estimated Cumulative Impact of External Factors on Portfolio Flows (Percent of GDP) ... with some countries likely to experience reduced inflows of 1–1.5 percent of annual GDP over the next two years. 4. External Financing Requirements (Percent of GDP) This could prove challenging for those with large external financing needs. Sources: Federal Reserve; and IMF staff estimates. Note: Data labels in the figure use International Organization for Standardization (ISO) country codes. EM = emerging market; Fed = Federal Reserve; QE = quantitative easing. Figure 1.15. Emerging Market Economy Capital Flows 23 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 the yield curve—in coordination with appropriate fiscal support and with consideration to the profit- ability of financial institutions and the functioning of the Japanese government bond market—should deflation pressure persist. Moreover, it is important for the Bank of Japan to continue to monitor the market liquidity and functioning of the Japanese government bond market and to consider appro- priate measures to alleviate shortages in the event of liquidity stress. Has the Search for Yield Gone Too Far? The low-interest-rate environment has stimulated a search for yield in markets, pushing investors beyond their traditional risk mandates. This has compressed spreads, reduced the compensation for credit and mar- ket risk in bond markets, contributed to low volatility, and facilitated the use of financial leverage. While these supportive financial conditions have helped boost growth, as intended, they have also raised the sensitiv- ity of the financial system to market risks. Prolonged normalization of monetary policy could extend these trends. Unless well managed, these rising medium-term vulnerabilities could lead to significant market disrup- tions if risk premiums and volatility decompress rapidly. Too Much Money Chasing Too Few Yielding Assets Has Created a Search for Yield After nearly 10 years of extraordinary monetary accommodation, as well as changing structural factors such as demographics and slower growth, the universe of global fixed income looks very different than before the global financial crisis. While the size of the fixed income market has exploded—one of the major investment- grade benchmark indices has increased from about $19.5 trillion in 2007 to $45.7 trillion in 2017—the portion of bonds with yields that meet investor targets has shrunk dramatically. In 2007, about 80 percent of the fixed income index ($15.8 trillion) yielded over 4 percent—the approximate required return for many absolute return investors such as pension funds and insurance companies (Figure 1.16, panel 1).17 But 17For example, the required return on investment for insurance companies = the guaranteed returns promised to policyholders + the cost of their equity * leverage. These numbers differ between markets. For the United States, this is 3.6 percent + 10 percent * 0.10 = 4.6 percent. For Europe, this is 2.3 percent + 10 percent * 0.07 = 3.0 percent. This assumes no additional sources of profit, such as underwriting margins, so the required return should be seen this proportion has now shrunk to less than 5 percent ($1.8 trillion) (Figure 1.16, panel 2).18 In the United States, this dearth of higher-yielding securities combined with the portfolio rebalancing effects of QE has resulted in a search for yield. There has been a marked shift of foreign investors out of their traditional positions in US Treasury bonds and agency securities and into higher-yielding US corporate bonds (Figure 1.16, panels 3 and 4). Non-US investors now rank among the largest holders of US corporate bonds, at nearly 30 percent of outstanding debt, up from 12 percent in 1990 and one quarter before the start of quantitative easing policies. Marginal demand has been especially pronounced among Asian investors, with flows from insurance and pension funds from Japan and Taiwan Province of China accounting for almost two-thirds of all foreign institutional flows into US investment-grade credit over the past three years. The Search for Yield Has Also Led to Greater Capital Flows and More Borrowing by Low-Income Countries In emerging market economies, the search for yield—combined with stronger growth and lower corporate vulnerabilities—has supported a notable rebound in portfolio inflows. Nonresident inflows of portfolio capital reached an estimated $205 billion in the year through August and are on track to reach $300 billion for 2017, more than twice the total observed during 2015–16 and on par with the strong pace of inflows from 2010–14 (Figure 1.17, panel 1). The primary beneficiaries of portfolio inflows have been large emerging market economies, including Colombia, Mexico, South Africa, and Turkey. Some have used this period to enhance policy buffers in the form of higher international reserves (Figure 1.17, panel 2). This has helped compress yields and spreads for sovereigns and firms, lifting asset valuations and external bond issuance (Figure 1.17, panels 3 and 4). Low-income countries have also benefited from the search for yield by expanding their access to interna- tional bond markets. Bond issuance has risen sharply since the start of 2017, with the total volume $7.4 bil- lion close to the record level in 2014 (Figure 1.18, panel 1). Despite strong global demand for yield, as an upper bound. Nevertheless, absolute return investors require historically high real rates. For pension funds, the required return is the discount rate applied to liabilities. 18Bank of America Global Broad Market Index. 24 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 low-income countries face less favorable borrowing conditions, reflecting less liquid markets, weaker credit profiles, and the lack of an issuance track record (Fig- ure 1.18, panel 2). Borrowing has generally been used to fund infrastructure projects, refinance debt, repay arrears, and increase budgetary flexibility.19 However, this borrowing has been accompanied by an underlying deterioration in debt burdens (Figure 1.18, panel 3). 19See IMF 2017a. In low-income countries, greater reliance on foreign borrowing leaves them vulnerable to a decompression of global risk premiums. This vulnerability reflects several factors, including higher total debt stocks and greater debt servicing needs and high exposure to flight-prone foreign asset managers and hedge funds. Low-income countries would be most at risk if adverse external conditions coincided with spikes in their external refinancing needs. Although near-term debt rollover needs are small, many low-income-country 0 2 4 6 8 10 12 14 0 10 20 30 40 0 1 2 3 4 5 6 7 8 –1–0 0–1 1–2 2–3 3–4 4–5 5–6 6–7 $15.8 trillion 7–8 >8
Corporate
Quasi and foreign government
Securitized/collateralized
Sovereign
Asia 4.2%
USA
yield 4.6%
Other Europe
4.3%
CAN 5.3%
GBR 4.7%
Latin America 7.3%
FRA 3.6%
DEU 2.1%
JPN 3.3%
EMEA 5.6%
ESP 2.2%
Global total
outstanding
$1.5 trillion
Yield (percent)
–1–0 0–1 1–2 2–3 3–4 4–5 5–6 6–7 7–8 >8
Yield (percent)
1. Global Investment-Grade Fixed Income Instruments, 2007
(Trillions of US dollars)
In 2007, a variety of asset classes generated returns in excess of
4 percent.
2. Global Investment-Grade Fixed Income Instruments, 2017
(Trillions of US dollars)
In 2017, corporate debt is the only significant asset class that provides
a comparable return.
3. Yields of US Dollar Corporate Bonds Outstanding
US corporate bonds make up the majority of the US dollar corporate
bond universe …
4. Holdings of US Corporate Bonds and Loans, by Investor Type
(Percent)
… drawing foreign investors beyond their traditional risk habitats.
Sources: Bank of America Merrill Lynch; Bloomberg Finance L.P.; Federal Reserve; Haver Analytics; and IMF staff estimates.
Note: Panels 1 and 2 are based on the Bank of America Global Bond Market Index. Data labels in the figure use International Organization for Standardization (ISO)
country codes. EMEA = Europe, Middle East, and Africa.
Figure 1.16. Global Fixed Income Markets and US Corporate Credit Investor Base
$1.8 trillion
2008 09 10 11 12 13 14 15 16 17
Foreign sector Insurance companies Banks and broker dealers
Mutual funds Households/hedge funds

25
C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
issuers face a significant repayment hump after 2021
(Figure 1.18, panel 4). Indeed, annual principal and
interest repayments (as a percent of GDP or inter-
national reserves) have risen above levels observed in
regular emerging market economy borrowers.
Credit and Market Risks Are Increasingly
Being Mispriced
Low yields, compressed spreads, abundant financ-
ing, and the relatively high cost of equity capital
have encouraged a buildup of financial balance sheet
leverage as corporations have bought back their equity
and raised debt levels (as discussed in the April 2017
GFSR). This means that the share of lower-rated com-
panies in major US, European, and global bond indi-
ces has increased (Figure 1.19, panel 1). This trend of
worsening credit quality also means that the estimated
default risk for high-yield and emerging market bonds
has remained elevated (Figure 1.19, panels 4 and 5).
Despite declining credit quality, the compensation
for credit risk in key corporate bond markets has
–100
–75
–50
–25
0
25
50
75
100
125
150
175
2011 12 13 14 15 16 17F
–200
–100
0
100
200
300
400
2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17
–5
0
5
10
15
20
25
30
35
40
45
50
So
ut
h
Af
ric
a
Tu
rk
ey
Co
lo
m
bi
a
M
ex
ic
o
Ch
ile
In
di
a
Po
la
nd
Br
az
il
In
do
ne
si
a
Ch
in
a
Ru
ss
ia
Portfolio debt Portfolio equity
Reserves Other investments
EM debt inflows
EM equity inflows
–450
–350
–250
–150
–50
50
150
250
350
450
550
2011 12 13 14 15 16 17F
Amortizations/buybacks Gross issuance
Coupons Net financing
Amortizations/buybacks Gross issuance
Coupons Net financing
1. Nonresident Portfolio Flows to Emerging Markets
(Billions of US dollars, four-quarter rolling sum)
Portfolio flows to emerging markets have rebounded in recent
quarters.
2. Cumulative Nonresident Capital Inflows and Change in
Gross Reserves, 2010:Q1–17:Q1
(Percent of GDP)
Some emerging markets have used foreign inflows to build reserve
buffers.
3. Hard Currency Sovereign Issuance
(Billions of US dollars)
Emerging market sovereign gross and net issuance is at record levels.
4. Hard Currency Corporate Issuance
(Billions of US dollars)
Corporate gross issuance is back to 2013–14 levels, but net issuance
remains subdued.
Sources: Haver Analytics; Institute of International Finance; JPMorgan Chase & Co.; and IMF staff estimates.
Note: Panel 2 uses four-quarter sum of GDP to 2017:Q1. Panels 3 and 4 are JP Morgan estimates. Panel 4 omits direct investment and financial derivative liabilities.
EM = emerging market; F = forecast.
Figure 1.17. Emerging Market Economies: Debt Issuance, Portfolio Flows, and Asset Prices
84
205 225 186
12 63
110
3
20
0
13
1
57 58

26
G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
actually fallen. One way to gauge this is to measure
the amount of spread per unit of corporate leverage
paid to investors. For every increase in the lever-
age multiple (measured by debt to earnings before
interest, taxes, depreciation, and amortization), the
spread received has declined sharply for both US
dollar–denominated and emerging market bonds
(Figure 1.19, panel 2). A decomposition of bond
yields suggests that the amount of spread left for mar-
ket risk has fallen, particularly for high-yield bonds
(Figure 1.19, panels 3–5). Similarly, other estimates
of market risk premiums in bond markets suggest
that compensation has declined steadily over time
(Figure 1.19, panel 6). To reach the average levels
from 2000 to 2004, market risk and term premi-
ums would need to rise about 200 basis points for
investment-grade bonds and about 450 basis points
for high-yield bonds. Market risk and term premiums
would need to rise about 375 basis points for emerg-
ing market bonds.
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
6
7
8
9
10
11
12
3
4
5
6
7
8
9
10
11
12
2009 10 11 12 13 14 15 16 17YTD
Africa
Asia
Latin America
Number of issuers (right scale)
0
1
2
3
4
5
6
7
8
9
2018 19 20 21 22 23 24
0
20
40
60
80
100
0 5 10 15 20 25 30 35
Pu
bl
ic
g
ro
ss
d
eb
t (
pe
rc
en
t o
f G
D
P)
Public interest expenses to revenues (percent)
Cameroon
Rwanda
Honduras
Vietnam
Nigeria
Mozambique
Côte d’Ivoire
Ethiopia
Ghana
Zambia
Kenya
Tanzania
Sources: Bloomberg Finance L.P.; Bond Radar; and IMF staff estimates.
Note: Sample includes 74 low-income countries that were both International Development Association and IMF Poverty Reduction and Growth Trust (PRGT) eligible as
of end-2014. Four countries (Bolivia, Mongolia, Nigeria, Vietnam) have graduated from the list of PRGT-eligible countries. Data labels use International Organization
for Standardization (ISO) country codes. EM = emerging market; YTD = year to date.
Figure 1.18. Low-Income Country External Borrowing and Vulnerabilities
1. International Sovereign Issuance of Low-Income
Countries by Region
(Billions of US dollars)
Low-income sovereign bond issuance has risen sharply in 2017,
nearing previous peaks.
2. Low-Income Country Coupons at Issuance and Secondary
Emerging Market Yields
(Percent)
Market access conditions improved recently, but remain less favorable
compared with other issuers.
3. Interest to Revenues and Public Debt, 2012–18
Debt burden indicators have deteriorated.
4. Sovereign International Bond Servicing Needs
(Billions of US dollars)
Tighter external financial conditions would affect those with large
rollover needs.
Oct. 2011 Aug. 12 Jun. 13 Apr. 14 Feb. 15 Dec. 15 Oct. 16 Aug. 17
EM B-rated sovereigns Individual country issuances
Rising issuance Stressed
issuance
Recovering
issuance
EM BB-rated sovereigns
Principal Interest

27
C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
–2.0
0
2
4
6
8
10
12
14
16
18
Ja
n.
2
00
7
Au
g.
0
7
M
ar
. 0
8
O
ct
. 0
8
M
ay
0
9
D
ec
. 0
9
Ju
l.
10
Fe
b.
1
1
Se
p.
1
1
Ap
r.
12
N
ov
. 1
2
Ju
n.
1
3
Ja
n.
1
4
Au
g.
1
4
M
ar
. 1
5
O
ct
. 1
5
M
ay
1
6
D
ec
. 1
6
Ju
l.
17
–2
0
2
4
6
8
10
12
14
16
18
20
22
Ja
n.
2
00
2
Ja
n.
0
3
Ja
n.
0
4
Ja
n.
0
5
Ja
n.
0
6
Ja
n.
0
7
Ja
n.
0
8
Ja
n.
0
9
Ja
n.
1
0
Ja
n.
1
1
Ja
n.
1
2
Ja
n.
1
3
Ja
n.
1
4
Ja
n.
1
5
Ja
n.
1
6
Ja
n.
1
7
0
20
40
60
1999 2001 03 05 07 09 11 13 15 17
Market risk premiums
Term premium
Default risk compensation
Risk-neutral Treasury yield
Total
–2
0
2
4
6
8
10
12
14
16
18
20
22
Ja
n.
2
00
0
Ja
n.
0
1
Ja
n.
0
2
Ja
n.
0
3
Ja
n.
0
4
Ja
n.
0
5
Ja
n.
0
6
Ja
n.
0
7
Ja
n.
0
8
Ja
n.
0
9
Ja
n.
1
0
Ja
n.
1
1
Ja
n.
1
2
Ja
n.
1
3
Ja
n.
1
4
Ja
n.
1
5
Ja
n.
1
6
Ja
n.
1
7–1
0
1
2
3
4
5
6
7
8
9
10
11
12
Ja
n.
2
00
0
Ja
n.
0
1
Ja
n.
0
2
Ja
n.
0
3
Ja
n.
0
4
Ja
n.
0
5
Ja
n.
0
6
Ja
n.
0
7
Ja
n.
0
8
Ja
n.
0
9
Ja
n.
1
0
Ja
n.
1
1
Ja
n.
1
2
Ja
n.
1
3
Ja
n.
1
4
Ja
n.
1
5
Ja
n.
1
6
Ja
n.
1
7
0
100
200
300
400
500
600
700
800
2008 09 10 11 12 13 14 15 16 17
Global emerging markets
United States
Market risk premiums
Term premium
Default risk compensation
Risk-neutral Treasury yield
Total
Market risk premiums
Term premium
Default risk compensation
Risk-neutral Treasury yield
Total
Developed market US dollar high yield
Global US dollar investment grade, excluding
emerging markets
Emerging markets
United States Euro area Global
1. Quality Breakdown of the Investment-Grade Index
(Percent of sample with BBB rating)
A high proportion of ratings are clustered at the bottom end of the
investment-grade rating range.
2. Emerging Market and US Dollar Bond Spreads per Turn of
Leverage
(Basis points per turn of leverage)
Risk-adjusted spreads have compressed to postcrisis lows.
3. US Dollar Global Investment-Grade Bond (Excluding
Emerging Markets) Yield Decomposition
(Percent)
Risk premiums grind tighter for investment …
4. US Dollar Developed Market High-Yield Bond Yield
Decomposition
(Percent)
… and high-yield risk premiums fall to near new tights after an
energy-related pop in 2016.
5. US Dollar Emerging Market Bond Yield Decomposition
(Percent)
Emerging market bond risk premiums are also grinding lower …
6. Markets Plus Term Premiums for Emerging Market and
Developed Market Investment-Grade and High-Yield Bonds
(Percent)
… driven by declines in term and market risk premiums.
Sources: Bank of America Merrill Lynch; JPMorgan Chase & Co; Standard & Poor’s; and IMF staff calculations.
Note: Market risk premium is the difference between the observed monthly bond spread and the estimated default risk compensation. Default risk compensation is
estimated monthly by breaking down each index’s holdings into Standard & Poor’s (S&P) ratings buckets. Then, based on each bucket’s rating and average duration,
an average cumulative default probability is derived by referencing S&P’s ratings transition tables. These results are weighted by the duration and ratings distribution
of the corresponding index. Investment-grade spread, duration, and weightings are derived from the JPMorgan JULI ALL ex-EM index. High-yield data are derived
from the JPMorgan Developed Market High Yield index. Emerging market data are derived from the JPMorgan EMBI Global index. Loss given default is always
assumed to remain constant at 60 percent. Panel 5 includes both investment-grade and high-yield bonds.
Figure 1.19. US and Emerging Market Corporate Bond Spread Decomposition and Leverage

28
G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
Volatility Is Compressed
The bountiful liquidity provided by major cen-
tral banks through their QE programs, as well as
the expectation that central banks will react swiftly
to market stress, has further strengthened the link
between low risk premiums and low volatility. The
impact of economic and financial conditions on US
equity volatility is examined through an explanatory
model, which offers three main findings (Figure 1.20,
panel 1).20
• First, stable macroeconomic fundamentals have
reduced volatility, as captured by the volatility of
20The analysis is centered on the United States as the most repre-
sentative measure of global market volatility, given that the United
States accounts for over one-third of the global equity market and
dominates trading of implied volatility futures.
–1.0
–0.8
–0.6
–0.4
–0.2
0.0
0.2
0.4
0.6
0.8
2004–07
average
10 11 12 13 14 15 16 17
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
2010 11 12 13 14 15 16 17
0
5
10
15
20
25
30
35
40
2010 11 12 13 14 15 16 17
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
2010 11 12 13 14 15 16 17
Top 50
Bottom 300
Top 50
Bottom 300
Top 50
Bottom 300
Macroeconomic fundamentals
Corporate performance
Funding and liquidity conditions
External spillovers
VIX index
Model-fitted VIX index
1. Drivers of Declining Equity Volatility
(Z-score, number of standard deviations)
Equity volatility touched record lows in 2017.
2. Realized Volatility of Individual Stocks
(US/S&P 500 stocks, 90-day historical volatility)
S&P 500 index volatility is suppressed by large firms …
3. Net Income
(Percent of assets, four-quarter moving averages)
… whose earnings are stronger and more stable …
4. Dividends and Stock Repurchases
(Percent of assets, four-quarter moving averages)
… and whose payouts are more generous.
Sources: Bloomberg Finance L.P.; and IMF staff calculations.
Note: The Chicago Board Options Exchange Volatility Index (VIX) model is an ordinary least squares regression using quarterly data since 2004:Q1. Macroeconomic
fundamentals include US GDP growth and the rolling 12-month standard deviation of the Citi US Economic Surprise Index. Corporate performance includes net
income to assets and payouts to assets for Standard & Poor’s (S&P) 500 firms. Funding and liquidity conditions include the TED spread (the difference between the
interest rates on interbank loans and on short-term US government debt, “T-bills”); average euro, Japanese yen, and British pound one-year cross-currency basis
swap rate; and supply of US Treasuries net of Federal Reserve purchases. External spillovers include the average of 10-year Greek, Italian, Portuguese, and Spanish
yield spreads to the German 10-year yield. The VIX is used as the dependent variable in the volatility model.
Figure 1.20. Long-Term Drivers of the Low-Volatility Regime

29
C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
economic surprises and the strength of underlying
growth. Accommodative monetary policy has helped
support this economic environment.
• Second, the accommodative funding and liquidity
conditions provided by monetary policy have left
volatility lower than in previous cycles.
• Third, corporate performance has remained stable and
contributed to steady investor earnings expectations
and reduced volatility.
This steady corporate performance—and associated
low realized volatility measures—has been driven in
part by large-cap companies (Figure 1.20, panel 2).
The market performance of large-cap companies has
been underpinned by stronger and more resilient earn-
ings (Figure 1.20, panel 3). At the same time, how-
ever, cash-rich US corporations have used payouts via
dividends and stock repurchases to smooth equity valu-
ations and compress volatility (Figure 1.20, panel 4).
With payouts rising to a high percentage of assets, this
tool may be less available to smooth earnings. Finally,
increased dispersion of returns across sectors, which
may reflect potential policy shifts in the United States
and abroad, has also contributed to reduced volatility
of the overall index.
Low Volatility, Financial Leverage, and Liquidity
Mismatches Could Amplify a Market Shock
Low volatility can increase the sensitivity of the
financial system to market risk. First, in standard
portfolio risk models, low volatility enables investors
to increase their exposure to financial assets and so
their sensitivity to market risk. Second, low volatility
can create incentives for investors to increase financial
leverage, which collectively can amplify market shocks.
An example of this effect is the increased popularity
of so-called volatility-targeting investment strategies
(Figure 1.21, panel 1). These strategies seek to keep
expected portfolio volatility to a specific targeted level.
Lower market volatility (in both global equity and
bond markets) then means that greater financial lever-
age is needed to meet volatility targets (Figure 1.21,
panel 2).21
However, during volatility spikes, these strategies
can lead to significant asset sales to pare back leverage.
21Derivatives such as equity index futures are commonly used
to achieve greater financial leverage by volatility-targeting invest-
ment strategies.
Such an episode took place in August 2015,22 when a
representative volatility-targeting investment strategy
cut its global equity exposure drastically (Figure 1.21,
panel 3).23 The size of US equity holdings held by
volatility-targeting investment strategies may be larger
than $0.5 trillion today.24 Although this is less than
2.5 percent of the market capitalization of all US
publicly traded equities, the trading volume related
to deleveraging from these trading strategies could
be much larger, particularly at times of equity mar-
ket stress.25
The low-interest-rate environment has also raised
bond market risk. Low interest rates have reduced
coupons of newly issued bonds. While this has been
a boon for issuers, helping to reduce debt servicing
costs, it has come at the price of higher market risk for
investors. The prices of those bonds are more sensitive
to changes in interest rates (increasing their duration).
This market risk is illustrated in Figure 1.22, panel 1,
which simulates the impact of an immediate 100 basis
point shock on long-term interest rates. The analysis
shows that this impact has increased over time as dura-
tion has increased. Losses in bond funds might lead to
outflows from asset managers. Indeed, the sensitivity
of outflows appears to have increased in relation to
periods of large negative returns in US high-yield bond
funds (Figure 1.22, panel 2). A significant outflow
might trigger sales of riskier and less liquid assets held
by open-end mutual funds, which could lead to sub-
stantial changes in the price of these instruments and
22The Chicago Board Options Exchange Volatility Index (VIX)
increased sharply to 40.7 percent on August 24, 2015, its highest
level since September 2011, from 13.0 a week earlier. While rising
concerns about a hard landing in China amid a significant decline
in oil prices were major drivers of the increase in market volatility,
market participants’ concern about a perceived end to the Federal
Open Market Committee quantitative easing policy may have also
played a major role in the equity market sell-off.
23The Standard & Poor’s (S&P’s) 500 index exposure for a repre-
sentative volatility-targeting investment strategy uses the AQR Risk
Parity Fund mutual fund as its proxy portfolio.
24This estimate assumes that the universe of volatility-targeting
investment strategies holds on average a portfolio in which global US
equities account for 60 percent of the exposure and bonds account
for 40 percent. The result is also adjusted by an estimated leverage
number based on the volatility targets of different volatility-targeting
investors. US equity exposure is assumed to be about half of the
exposure to global equities. This is similar to the average geographic
breakdown of equity investments in the AQR Risk Parity Fund over
the past two years.
25Chandumont 2016 estimates that selling from volatility-
targeting funds accounted for between 9 and 16 percent of all
trading volume in S&P 500 futures during August 24–26, 2015.

30
G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
affect the value of these assets held by other investors.
Figure 1.22, panel 3 shows that mutual funds hold
a greater share of the high-yield bond market than
in the past.
Prolonged normalization of monetary policy could
mean continued low volatility and a further buildup of
exposures, duration, and financial leverage. This would
make the financial system even more sensitive to mar-
ket risk, storing up medium-term vulnerability.
Efforts Are Needed to Help Lessen Stability Risks
Regulators should be attentive to the potential for
a substantial increase in asset market volatility to con-
tribute to destabilizing feedback effects such as asset
fire sales and adverse liquidity and leverage spirals. To
lessen these risks, financial regulators should continue
working to ensure that financial institutions maintain
robust risk management standards at all points in the
credit, business, and interest rate cycles. In addition,
20
15
:Q
1
Q
2
Q
3
Q
4
16
:Q
1
Q
2
Q
3
Q
4
17
:Q
1
Q
2
Investment Strategy Volatility Target
(percent)
Flexibility to Deviate
from Volatility Target
AUM Mid-2017 Growth in AUM Past
Three Years (percent)
Variable Annuities 8–12 Low $440 billion 69
CTA/Systematic Trading 15 Medium $220 billion 19
Risk Parity Funds 10–15 Medium–high $150–$175 billion …
0
0.5
1.0
1.5
2.0
2.5
0
5
10
15
20
25
2012 13 14 15 16 17
Global equity index volatility (left scale)
Global bond index volatility (left scale)
Leverage of 60/40 portfolio with a 12 percent volatility target
(right scale)
0
20
40
60
80
100
120
10
15
20
25
30
35
40
45
Exposure to global non-US equities (right scale)
Exposure to US equities (right scale)
VIX index (maximum quarterly level, left scale)
Sources: Annuity Insights; Barclays Capital; BarclayHedge; and IMF staff calculations.
Figure 1.21. Leveraged and Volatility-Targeting Strategies
1. The Growth of Volatility-Targeting Investors
2. Leverage for a Theoretical Volatility-Targeting Investment
Portfolio1
(Sixty-day moving average)
Lower volatility drives investors to increase financial leverage to meet
their return and volatility targets …
3. Global Equity Exposure for a Representative Volatility-
Targeting Investment Portfolio2
(Percent/net asset value)
… leading to rising equity exposures that are prone to sell-offs during
volatility spikes.
Sharp reduction in equity
exposures as volatility spiked in
August 2015
Sources: Bloomberg Finance L.P.; Federal Reserve; Investment Company Institute; and IMF staff calculations.
Note: AUM = assets under management; CTA = Commodity Trading Advisor; VIX = Chicago Board Options Exchange Volatility Index.
1The leverage calculation for a theoretical volatility-targeting investment strategy assumes a theoretical investment portfolio consisting of 60 percent global
equities/40 percent bonds and an annual return volatility target of 12 percent. Leverage is defined as total investment exposure divided by the net asset value of the
portfolio. The calculation uses a 60-day realized volatility moving window on the returns of equity and bond investments. The MSCI World Index is used as the proxy
for equity investments; the Bloomberg Barclays Global Aggregate Total Return Value Unhedged index is used as the proxy for bond investments.
2The S&P 500 index exposure for a representative volatility-targeting investment strategy uses the AQR Risk Parity mutual fund as its proxy portfolio. The exposure
data are obtained using Bloomberg’s port function and reflect the percentage exposure of the fund’s portfolio to equity index futures as a percentage of market value.

31
C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
supervisors, regulators, and firm management should
closely monitor and assess financial institutions’
exposure to asset classes where there are indications
that the search for yield has contributed to valua-
tion pressure.
There is also a need for regulators to endorse a clear
and common definition of financial leverage in invest-
ment funds and to improve data transparency, partic-
ularly with respect to derivatives. Lack of progress on
regulation on the use of derivatives is a concern given
that the use of financial leverage through derivatives
appears to be on the rise as fund managers seek to
enhance low yields, particularly in strategies that target
a specified level of price volatility.
Policymakers should continue to strengthen supervi-
sory frameworks relating to liquidity risk management.
This could be done by building on recent initiatives
and recommendations to include greater flexibility in
redemption and dealing frequency,26 marking illiquid
26See US SEC (October 2016), FSB (January 2017), IOSCO
(July 2017), and UK FCA (February 2017).
Projected UK losses (left scale)
Projected euro area losses (left scale)
Projected US losses (left scale)
Percent loss (right scale)
United States (left scale) Europe (right scale)
1. Estimated Loss to Fixed-Income Mutual Funds Following a
100 Basis Point Shock to Interest Rates
Higher duration leaves investors more vulnerable to interest rate risk …
0
100
50
150
Bi
lli
on
s
of
U
S
do
lla
rs
Pe
rc
en
t
250
200
300
350
0
4
2
3
1
5
6
7
8
1994–95 99–2000
(Taper tantrum)
2004–06 Jun. 13 Mar. 17
Sources: Bloomberg Finance L.P.; EPFR Global; Federal Reserve; Investment Company Institute; and IMF staff estimates.
Note: In panel 1, data are based on prior periods of US monetary policy tightening starting in February 1994, July 1999, July 2004, and December 2015 and periods of large
interest rate moves since the global financial crisis. The Barclays Capital Global Aggregate index is used as a proxy for duration of an average fixed-income portfolio. Total
fixed-income mutual fund assets are used to calculate the dollar losses from a parallel 100 basis point increase in interest rates. Panel 2 shows periods when cumulative
losses have exceeded 5 percent. There have been only four periods over the past decade when cumulative monthly losses on US high-yield bond benchmarks have
exceeded 5 percent—a typical threshold used by investors when implementing stop-loss strategies. These risk management strategies are commonly used by investors to
reduce their holdings in risky assets if prices breach certain prespecified loss limits. By closing out the position, the investor is hoping to avoid further losses.
Figure 1.22. Vulnerability of the US Corporate Credit Investor Base to Shocks
2. Flows and Performance of US High-Yield Bond Mutual Funds
(Periods when cumulative losses exceeded 5 percent)
… at a time when there is greater sensitivity of investor outlows.
Ra
tio
: o
ut
flo
w
s
to
p
er
fo
rm
an
ce

0.0
0.6
0.4
0.5
0.2
0.1
0.3
0.7
0.8
0.9
1.0
–35 –30 –25 –20 –15 –10 –5
Cumulative returns (percent)
0
3. Mutual Funds Holdings as Share of Total High-Yield Bond Market
(Percent)
Liquidity mismatch risk is also a concern amid the rise in illiquid assets
held by mutual funds globally.
15
20
25
30
35
0
5
10
15
20
25
2008 09 10 11 12 13 14 15 16
Sep–Nov 2008
Jun–Sep 2015
Aug–Sep 2011
Nov 15–Jan 2016
Rising sensitivity of investor
outflows to periods of large losses

32
G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
assets to market, and the treatment of institutional
investors, as well as through better guidance on the
use of particular risk management tools and enhanced
disclosure requirements.
For borrowers in frontier markets and low-income
countries, authorities should develop institutional capac-
ity to deal with the risk that accompanies increased issu-
ance of marketable debt securities. Authorities should
formulate a comprehensive debt management strategy
that incorporates exchange rate, interest rate, and liquid-
ity risks associated with the issuance of external debt
and explore liability management operations to mitigate
refinancing risk.27 Authorities should ensure efficient use
of the borrowed funds by strengthening public invest-
ment management. They should also enhance investor
relations programs to better understand and inform the
international investment community regarding their
debt issuance strategy.
The Rise in Leverage
Leverage in the nonfinancial sector has increased since
2006 in many G20 economies amid easy financing
conditions. While this has helped facilitate the recovery
in aggregate demand, it has also made the nonfinancial
sector more sensitive to changes in interest rates. Private
sector debt service burdens have increased in several
major economies as leverage has risen, despite declining
borrowing costs. Debt servicing pressure could mount
further if leverage continues to grow and could lead to
greater credit risk in the financial system. China has
seen a rapid buildup in leverage, so the recent derisking
measures are a welcome step. Yet continued rapid credit
growth and accumulated vulnerabilities at smaller banks
make it challenging to fully address systemic risks.
Group of Twenty Nonfinancial Sector Leverage
Aggregate G20 Debt-to-GDP Ratios Are Higher than
before the Global Financial Crisis
Among G20 economies, total nonfinancial sector
debt—borrowing by governments, nonfinancial
companies, and households from both banks and
bond markets—has risen to more than $135 trillion,
or about 235 percent of aggregate GDP (Figure 1.23,
panel 1).28 This partly reflects economic develop-
27See IMF 2017b.
28G20 aggregates are based on the 19 individual economies in the
group (the 20th member is the European Union).
ments since the global financial crisis. The rise in sov-
ereign debt is largely due to the downturn in GDP,
but is also due in part to the necessary actions taken
by governments to stabilize economies and financial
sectors. Private sector credit growth has helped facil-
itate the subsequent recovery in aggregate demand,
and so has cushioned economic growth against
further downside risks. But higher debt has made
the nonfinancial sector more sensitive to changes in
interest rates.
In G20 advanced economies, the debt-to-GDP
ratio has grown steadily over the past decade and
now amounts to more than 260 percent of GDP. In
G20 emerging market economies, leverage growth
has accelerated in recent years. This was driven largely
by a huge increase in Chinese debt since 2007,
though debt-to-GDP levels also increased modestly
in other G20 emerging market economies (Fig-
ure 1.23, panel 2).
Overall, about 80 percent of the $60 trillion
increase in G20 nonfinancial sector debt since 2006
has been in the sovereign and nonfinancial corporate
sectors (Figure 1.23, panel 3). Much of this increase
has been in China (largely in nonfinancial companies)
and the United States (mostly from the rise in general
government debt). Each country accounts for about
one-third of the G20’s increase. Average debt-to-GDP
ratios across G20 economies have increased in all three
parts of the nonfinancial sector (Figure 1.23, panel 4).
There has also been a broad increase in nonfinancial
debt-to-GDP ratios across individual G20 econo-
mies since 2006; only Argentina and Germany have
experienced a decline in total nonfinancial sector debt
to GDP (Table 1.1). In some economies, individual
sectors have deleveraged. For example, household debt
to GDP fell in Germany and the United States, in
particular. Nonfinancial corporate leverage declined the
most in Argentina, Japan, and the United Kingdom.
But in the majority of cases in the G20, nonfinancial
debt-to-GDP ratios have risen.
While gross liabilities have risen, the development
of net debt—gross debt minus financial assets—has
varied across the nonfinancial sector in G20 advanced
economies (Figure 1.23, panel 5). General government
net debt rose along with gross debt over the decade
since 2006. Nonfinancial private sector net debt, how-
ever, fell as savings and higher asset prices helped build
up financial assets more quickly than liabilities. This,
in turn, has helped support the recovery in spending

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C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
G20 emerging market economies,
excluding China
G20 advanced economies
China
Nonfinancial companies
Households
General government
Nominal GDP
Debt (left scale)
Cash (right scale)
1. Gross Debt and GDP
(Trillions of US dollars)
Debt has been rising more quickly than GDP …
3. Change in Gross Debt, 2006–16
(Trillions of US dollars)
… and in sovereigns and firms …
0
40
20
80
60
100
120
140
Sources: Bank for International Settlements; Bloomberg Finance L.P.; Haver Analytics; IMF, World Economic Outlook database; and IMF staff calculations.
Note: Data are adjusted for foreign exchange movements by converting to US dollars at the end-2016 exchange rate. Advanced economy nonfinancial corporate debt is
shown net of estimated intercompany loans. In panel 3, OTH = other Group of Twenty (G20) economies. Panel 4 shows the average debt-to-GDP ratio across the G20
economies, by sector. Panel 5 shows debt minus financial assets as a percent of GDP. Panel 6 is based on a sample of more than 2,600 nonfinancial companies in
continental Europe, Japan, the United Kingdom, and the United States. Each dot shows average debt and cash to assets for the same 50 firms. Data labels in the figure use
International Organization for Standardization (ISO) country codes.
Figure 1.23. Group of Twenty Nonfinancial Sector Credit Trends
2. Gross Debt-to-GDP Ratios by Region
(Percent)
… largely in advanced economies and China …
0
20
40
60
80
100
500 1,000 1,500 2,000
4. Average Gross Debt-to-GDP Ratios by Sector
(Percent)
… with debt-to-GDP ratios above precrisis levels.
30
40
50
60
70
20
80
92 94 96 98 2000 02 04 06 08 10 12 141990 16
92 94 96 98 2000 02 04 06 08 10 12 141990 16 92 94 96 98 2000 02 04 06 08 10 12 141990 16
5. Advanced Economy Net Debt-to-GDP Ratios by Sector
(Percent)
Private sector financial assets have risen …
6. Advanced Economy Nonfinancial Corporate Debt and Cash
(Percent of assets)
… but cash is unevenly distributed among firms.
5
10
15
20
25
30
0
35
0 2,500
General government Nonfinancial companies
USA
11.1
OTH
6.8
OTH
3.0
OTH
4.7
AUS
0.7
USA
1.5
IND 0.8
USA
4.5
CAN 0.7
CAN
0.7
JPN
2.7
GBR
1.4
CHN
4.4
CHN
14.4
Households
CHN
3.9
40
50
60
70
80
90
20
06 08 10 12 14 16
General
government
–60
–50
–40
–30
–20
20
06 08 10 12 14 16
–280
–260
–240
–220
–200
–180
–160
20
06 08 10 12 14 16
Nonfinancial
companies
Households Firms with the highest debt
have the lowest cash
Sample (ordered by debt-to-assets ratio)
50
100
150
200
250
300
General government
Households
Nonfinancial companies

34
G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
and GDP. But it is important not to draw too much
comfort from this development. While debt accumula-
tion is not necessarily a problem, one lesson from the
global financial crisis is that excessive debt that creates
debt servicing problems can lead to financial strains.
Another lesson is that gross liabilities matter. First, in
a period of stress, it is unlikely that the whole stock of
financial assets can be sold at current market values—
and some assets may be unsellable in illiquid condi-
tions. Second, the aggregate data used here do not
account for differences in the distribution of assets and
liabilities. For example, the younger population might
have a greater proportion of debt in the household
sector, while the older population might have a greater
proportion of financial assets.
A similar argument can be made about cash
holdings in nonfinancial companies. Although cash
holdings may be netted from gross debt at an individ-
ual company—because that firm has the option to pay
back debt from its stock of cash—it could be mislead-
ing to do so in the aggregate data generally used in this
section. This is because the distribution of debt and
cash holdings differs between companies. Figure 1.23,
panel 6, which is based on debt and cash stocks held
by a sample of more than 2,600 European, Japanese,
and US companies, shows that those with higher debt
also tend to have lower cash holdings and vice versa.
Although G20 gross private nonfinancial debt has
increased in the aggregate, the reasons for higher
leverage differ across sectors. For example, changes in
household leverage appear to be broadly associated
with lower borrowing costs and house price move-
ments (Figure 1.24, panel 1). Higher house prices,
driven up by buoyant market conditions and risk
appetite, mean that not only is more borrowing needed
to purchase properties but also that more collateral is
available to support the increased borrowing. Lower
interest rates make new borrowing more attractive for
households. Chapter 2 examines household indebted-
ness in more detail. It finds that household debt has
continued to grow over the past decade across a broad
set of countries. It also concludes that high growth in
household debt in the medium term is associated with
a greater probability of a banking crisis.
The increase in corporate debt has taken place
during loose financing conditions, just as during the
period before the global financial crisis (Figure 1.24,
panel 2). Low interest rates probably stimulated greater
demand for credit from companies as larger debt
became more affordable, leading to changes in capital
structures. Easy financing conditions—a combination
of low interest rates, buoyant market valuations, and
low volatility—have reduced the probability of default
as measured by credit models, which is likely to have
increased the willingness of lenders to supply credit to
companies.29
However, this contemporaneous default proba-
bility is based on current market conditions, which
might not last. If there are adverse shocks, a feedback
29Growth in private sector debt in some emerging market econ-
omies may also be linked to improvements in credit infrastructure
(such as increased use of credit registries and improvements in credit
risk evaluation) as well as policies to foster lending to small and
medium enterprises and financial inclusion.
Table 1.1. Sovereign and Nonfinancial Private Sector Debt-to-GDP Ratios
(Percent)
Advanced Economies Emerging Market Economies
JPN CAN USA GBR ITA AUS KOR FRA DEU CHN BRA IND ZAF TUR MEX RUS SAU ARG IDN
General
Government
2006 184 70 64 41 103 10 29 64 66 25 66 77 31 45 38 10 26 70 36
2016 239 92 107 89 133 41 38 96 68 44 78 70 52 28 58 16 13 54 28
Households
2006 59 74 96 90 36 105 70 44 65 11 14 10 39 9 12 8 12 4 11
2016 57 101 79 88 42 123 93 57 53 44 23 10 35 18 16 16 15 6 17
Nonfinancial
Corporations
2006 100 76 65 79 67 73 83 56 49 105 39 38 33 27 14 32 28 20 14
2016 92 102 72 73 71 79 100 72 46 165 44 45 37 67 28 52 50 12 23
Total 2006 343 221 225 210 205 187 183 164 180 142 118 125 104 81 64 49 66 93 61
2016 388 295 259 250 246 243 232 226 168 254 145 125 124 113 103 84 78 73 68
Sources: Bank for International Settlements; Haver Analytics; IMF, World Economic Outlook database; and IMF staff calculations.
Note: Dark shading denotes a higher debt-to-GDP ratio in 2016 than in 2006. The table shows debt at market values. Advanced economy nonfinancial corporate
debt is shown net of estimated intercompany loans where data are available. Data labels in the table use International Standardization Organization (ISO) codes.

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C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
loop could develop, which would tighten financial
conditions and increase the probability of default, as
happened during the global financial crisis. Thus the
low contemporaneous default probability could mask
risks associated with the buildup of corporate lever-
age, a phenomenon that has been called the “volatility
paradox.”30
Higher Private Sector Debt Has Raised Servicing
Costs and Could Increase Vulnerabilities
While debt has generally increased relative to GDP,
it happened in a period of falling and low interest
rates. So what happened to debt affordability over this
period? This question is important because measures
of debt affordability tend to be good vulnerabil-
ity signals, particularly when debt levels are high.31
Although lower interest rates have helped lower sover-
eign borrowing costs, in most of the G20 economies
where companies and households increased leverage,
nonfinancial private sector debt service ratios—
defined as annualized interest payments plus income
amortization—also increased (Figure 1.25, panel 1).
Moreover, there are now several economies where
debt service ratios for the private nonfinancial sectors
are higher than average and where debt levels are also
high. Figure 1.25, panel 2, shows that this is partic-
ularly the case for the nonfinancial private sector in
Australia, Canada, and China, and for the household
sector in Korea (debt service ratios for households and
nonfinancial companies are available only for G20
advanced economies).
The distribution of debt within an economy’s corpo-
rate and household sectors is also important in assessing
payment pressures. While the aggregate data on debt
service ratios used here do not allow an examination of
the distribution, other work might shed some light on
this question. The April 2017 GFSR found (for com-
panies in the United States) a deterioration in interest
coverage ratios for those most indebted, particularly
in the energy sector. In emerging market economies,
however, commodity companies and industrials made
up a significant proportion of firms with weak interest
30See Adrian and Shin 2013 and Geanakoplos 2010 for a
discussion of the leverage cycle, and Brunnermeier and Sannikov
2014 and Adrian and Brunnermeier 2016 for a discussion of the
volatility paradox.
31Chapter 2 discusses household debt service capacity as a
vulnerability indicator. See also work at the Bank for International
Settlements on this issue, including Drehman, Juselius, and Korinek
2017; BIS 2017; and BIS 2012.
Economies with nominal house
price growth greater than 25
percent
(2006–16)
Economies with nominal house
price growth less than 25 percent
(2006–16)
–4
–3
2
–1
0
1
2
3
4
–2.0
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
2000 02 04 06 08 10 12 14 16
St
an
da
rd
d
ev
ia
tio
ns
fr
om
m
ea
n
Cr
ed
it
ga
p
(p
er
ce
nt
ag
e
po
in
ts
)
–20
–10
0
10
20
30
40
2. Average Nonfinancial Corporate Credit, Financing Conditions, and
Default Probability
(Four-quarter moving average)
Figure 1.24. Group of Twenty Nonfinancial Private Sector
Borrowing
1. Change in Household Gross Debt to GDP, 2006–16
(Percentage points)
Household debt has risen broadly with house prices.
Corporate debt has built up with easy financing conditions.
Financing
conditions
(right scale)
Model-based
probability of
default
(right scale)
Credit
(left scale)
CH
N
CA
N
KO
R
AU
S
TU
R
ID
N
M
EX ZA
F
D
EU FR
A
IT
A
JP
N
G
BR US
A
IN
D
BR
A
RU
S
Sources: Bank for International Settlements; Bloomberg Finance L.P.; Haver Analytics;
Moody’s CreditEdge; Organisation for Economic Co-operation and Development; and
IMF staff calculations.
Note: In panel 1, house price growth is from 2008 in Brazil; from 2010 in China, India,
and Turkey; and is not available for Argentina and Saudi Arabia. Panel 2 shows the
average Group of Twenty: corporate debt-to-GDP gap, financing conditions (average of
corporate borrowing rates, book-to-market ratios, and implied volatility), and
probability of default from the Moody’s KMV model (based on a sample of more than
41,000 companies). Data labels in the figure use International Organization for
Standardization (ISO) country codes.

36
G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
Figure 1.25. Group of Twenty Nonfinancial Private Sector Credit and Debt Service Ratios
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10 11 12
Years since start of boom
–10
0
10
20
30
40
50
1 2 3 4 5 6 7 8 9 10
1. Change in Private Nonfinancial Sector Debt and Debt Service Ratios, 2006–16
2. Debt Service Ratios and Debt, 2016
(Percent)
3. Change in Credit-to-GDP Ratio
(Percentage points)
4. Cumulative Real House Price Growth
(Percent)
–6
–4
–2
0
2
4
6
8
10
Ch
an
ge
in
d
eb
t s
er
vi
ce
r
at
io
(p
er
ce
nt
ag
e
po
in
ts
)
Change in debt to GDP (percentage points)
200 300 400 500 600 700
Nonfinancial companies
D
ev
ia
tio
n
fr
om
m
ea
n,
p
er
ce
nt
ag
e
po
in
ts
Gross debt to income (percent)
40 60 80 100 120 140 160 180
Households
D
ev
ia
tio
n
fr
om
m
ea
n,
p
er
ce
nt
ag
e
po
in
ts
Gross debt to income (percent)
Greater debt
payment pressure
Years since start of boom
0 50 100 150 200 250
Nonfinancial private sector
D
ev
ia
tio
n
fr
om
m
ea
n,
p
er
ce
nt
ag
e
po
in
ts
Gross debt to GDP (percent)
–40 –20 0 20 40 60 80 100 120
Debt service ratios have increased with higher leverage, despite low interest rates.
Debt service ratios in some countries are now at high levels …
… in economies with credit booms … … and house price growth.
Sources: Bank for International Settlements; Bloomberg Finance L.P.; national statistical offices; Organisation for Economic Co-operation and Development; and IMF staff
calculations.
Note: Debt service ratios are defined as annualized interest payments plus amortizations as a percentage of income, as calculated by the Bank for International Settlements.
In panel 1, the size of the circles is proportional to debt to GDP in 2016. In panel 2, income is gross disposable income plus interest payments (plus dividends paid for firms).
Panel 3 shows Group of Twenty economies with higher demeaned nonfinancial private sector debt service ratios and debt levels against past booms. Past booms are for a
sample of 43 advanced and emerging market economies where the credit-to-GDP gap rose above 10 percent. The start and end dates of the booms are defined as periods
when the credit gap was above 6 percent. Financial crisis dates were taken from Laeven and Valencia 2012. Data labels in the figure use International Organization for
Standardization (ISO) country codes.
Canada
Average of credit
booms that did not lead
to financial crises
–10
–5
0
5
10
15
USA
GBR
KORJPN
ITA
DEU
FRA
CAN
AUS
–3
–2
–1
0
1
2
3
4
5
6
TUR
ZAF
RUS
MEX
IDN
IND
CHN
BRA
USA
GBR
ITA
KOR
JPNDEU
FRA
CAN
AUS
TUR
ZAF
RUS
MEX
IDN
IND
CHN
BRA
USA
GBR
KOR
JPN ITA
DEU
FRA
CAN
AUS
–2.0
–1.5
–1.0
–0.5
0.0
0.5
1.0
1.5
2.0
USA
GBR
KOR
JPNITA
DEU
FRA CAN
AUS
Australia
China
Korea
Average of credit
booms that led to
financial crises
China
Australia
Canada
Korea

37
C H A P T E R 1 I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
coverage ratios. Similarly, ECB 2017 shows that the dis-
tribution of household debt service ratios reveals greater
vulnerability among those that had more recently taken
out a mortgage to finance a house purchase than was
evident from the aggregate figure.
Although not all credit booms lead to recessions, it
is interesting to compare the credit booms in econo-
mies most likely to face payment pressures with past
experience. While the boom in Australia is similar to
the average of past credit booms that did not lead to
a financial crisis, the boom in Canada has been longer
than the average of these benign booms, and the boom
in China has been steeper than the average of past
credit booms that did coincide with a financial crisis
(Figure 1.25, panel 3). In addition, in three of the
economies with the highest debt service ratios, there
has been a steep increase in real house price valuations
(Figure 1.25, panel 4).
Experience has shown that a buildup in leverage
associated with a run-up in house price valuations can
develop to a point that they create strains in the non-
financial sector that, in the event of a sharp fall in asset
prices, can spill over to the economy. For example,
Chapter 2 finds that the relationship between future
GDP growth and household debt is driven mostly by
mortgage debt. This could be because of the procycli-
cality of home equity lines of credit, or more generally
because of wealth effects that lead households to cut
consumption when the value of their housing assets
declines.32
Overall, there are now several major economies
where debt servicing pressure in the private nonfinan-
cial sector is already high. Weaker households and
companies in these countries could have trouble repay-
ing their debt if interest rates rise or if incomes fall.
Policies Are Needed to Reduce Vulnerabilities in the
Private Nonfinancial Sector
Policymakers should address the risks from contin-
ued increases in debt and leverage across sectors by
drawing on, and enhancing where needed, an appro-
priate mix of macroprudential and microprudential
policies, preemptive regulatory measures, and close
monitoring of balance sheets.
Higher household debt burdens should be reduced
where debt servicing pressures are already high and
should not grow further where debt servicing is
32See also Mian and Sufi 2011 and Schularick and Taylor 2012.
currently manageable but debt levels are elevated.
This can be achieved through a combination of
measures, including limits on debt-service-to-income
and loan-to-value ratios, and measures to restrict loan
contracts. Some countries have undertaken measures to
address high house price valuations and deter further
buildup of household debt. Policy measures, however,
must carefully balance minimizing the medium-term
risks to financial stability while not harming the
potential long-term benefits of financial inclusion and
development.
Policymakers should vigilantly monitor nonfinancial
corporate leverage. Macroprudential measures extended
through banks (such as sectoral capital requirements or
risk weights on foreign currency credit) could also be
considered to reduce or prevent a further buildup in cor-
porate debt. In addition, tax reforms that reduce incen-
tives for debt financing could help attenuate the risk of
a further buildup in leverage and may even encourage
firms to lower existing tax-advantaged leverage. More
broadly, measures to foster smooth corporate delever-
aging should be deployed where needed, including by
strengthening corporate restructuring mechanisms.
China: From Derisking to Deleveraging—
Challenges Ahead
The rapid rise in nonfinancial sector leverage in
China in recent years, along with the size, complex-
ity, and pace of growth of its financial system, point
to continued financial stability risks. Banking sector
assets are now 310 percent of GDP, nearly three
times the emerging market average and up from
240 percent at the end of 2012. Rapid increases in
intrafinancial-system credit have been an important
factor in this growth (see Figure 1.26, panel 1). This
reflects both the growing use of short-term wholesale
funding to boost leverage and profits (Figure 1.26,
panel 2) and shadow credit to firms and other non-
financial borrowers (Figure 1.26, panels 3 and 4),
particularly by small and medium-sized banks.33 This
33Shadow credit refers to banks’ nonloan, nonbond credit to
nonfinancial borrowers. This includes assets that are on balance sheet
(trust beneficiary rights, specialized asset management plans, and
other structured assets) and off balance sheet (bank-sponsored wealth
management plans). Estimates of off-balance-sheet bank credit are
calculated as 65 percent of outstanding wealth management plans,
which deduct the portion of underlying plan assets that are claims
on financial or public sector counterparties, as reported in China
Bank Wealth Management Market Annual Report 2016.

38
G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
has increased the opacity of intermediation, increased
the use of unstable short-term funding, and raised
sensitivity to liquidity stress.
China recently introduced a range of prudential
and administrative measures to contain these vulner-
abilities. Efforts to derisk the financial system using
better-designed regulatory tools (such as the Macro-
prudential Assessment, or MPA) aim to slow growth in
banks’ supply of shadow credit, reduce dependence on
interbank funding, and contain regulatory arbitrage.34
34Among examples of such measures are the People’s Bank of
China’s inclusion of wealth management products in its MPA frame-
work, counting negotiable certificates of deposit toward the pru-
dential limit on interbank liabilities, and tightening corporate bond
collateral requirements for exchange-traded repurchase agreements.
On-balance-sheet shadow credit products at small and
medium-sized banks declined sharply in late 2016 and
early 2017. Growth in off-balance-sheet shadow credit,
in the form of wealth management products, has also
recently reversed by the largest amount in the post-
crisis period (Figure 1.27). This coincided with rising
interbank and bond market interest rates and stalling
corporate bond issuance.
Authorities Face a Delicate Balance between
Tightening Financial Sector Policies and Slowing
Credit Growth
Curbing shadow credit could have an out-
size impact on banks’ capacity to increase credit.
Bank-level data show that roughly half of lenders’
Figure 1.26. Chinese Banking System Developments
Big 5
Small and medium-sized banks
Total
Other assets Claims on financial sector
Claims on nonfinancial sector
Shadow credit: off balance sheet
Shadow credit: on balance sheet
New loans
Big 5 Small and medium-sized banks
1. Contribution to Bank Asset Growth
(Percent, year over year)
3. Net Increase in Private Nonfinancial Credit
(Trillions of renminbi)
4. Bank Shadow Credit: Net Increase and Ratio to Deposits
(Trillions of renminbi and percent)
2. Nondeposit Funding
(Maturing < 1 year, percent of total assets) Intrafinancial system credit has driven bank growth ... ... but also reflecting significant shadow credit ... ... particularly from small and medium-sized banks. ... increasing reliance on risky funding ... 2014 15 16 2014 15 16 –2 0 2 4 6 8 10 12 14 16 18 2014 15 16 17 5 10 15 20 25 30 35 40 2011 12 13 14 15 16 0 2 4 6 8 10 12 14 16 2014 15 16 Shadow credit as percentage of total new credit 41% 53%51% Intrafinancial system credit Sources: Haver Analytics; People’s Bank of China; SNL Financial; and IMF staff calculations. Note: Shadow credit refers to banks’ nonloan, nonbond credit to nonfinancial private borrowers, both on and off balance sheet. For a complete definition, please see footnote 33. Panels 2, 3, and 4 are based on publicly available financial statement data for 32 of China’s largest banking groups. 0 1 2 3 4 5 6 7 8 0 10 20 30 40 50 60 70 80 90 79 66 48 1313 9 Net increase (trillions of renminbi) Shadow credit to deposits (percent) 39 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 estimated credit in recent years was extended via such products.35 As shadow credit typically requires less capital and provisioning than regular loans, reduc- ing its growth would free up only enough capital to support a smaller increase in lending, leading to a net slowdown in the flow of total credit. For instance, if banks expanded shadow credit by 27 percent—the pace in 2016—their projected retained earnings would support total credit growth (loans and shadow credit) of 17 percent year over year, just above the actual growth rate in 2016. If banks instead kept shadow credit constant, increasing only loans, the same amount of retained earnings would support credit growth of 11 percent, in line with nominal GDP growth in the second quarter of 2017 (Fig- ure 1.28, panel 1). Banks face a trade-off between using retained earnings to address vulnerabilities or support credit growth.36 If some retained earnings are used to increase the pace of loss recognition, or increase capital and provisions against a modest portion of existing shadow products, credit capacity would decline further (Figure 1.28, panel 2). Balance sheet vulnerabilities from shadow credit would also recede only gradually at smaller banks, remaining elevated relative to the biggest banks (Figure 1.28, panel 3). Derisking Will Weigh on Some Banks’ Profitability and Business Models Shifting away from shadow credit products and interbank funding will improve bank balance sheets over time, but in the short term could also decrease bank profitability, weakening buffers at already vul- nerable banks and reducing capacity to expand credit. Bank earnings in China have fallen in recent years, driven by an uptick in provision expenses and lower net interest margins (Figure 1.29, panel 1). Small and medium-sized banks have sustained profitability in 35Based on publicly reported data for a sample of 32 of China’s largest banking groups. This calculation excludes corporate bonds held in banks’ securities portfolios. The total credit provision from these banks depicted is equivalent to roughly 90 percent of the total increase in nonfinancial credit in 2015 and 2016 (as measured by Total Social Financing flows). 36Banks can avoid this trade-off through recapitalization. Chinese banks have announced planned increases of RMB 66 billion in new common equity for 2017, or about 2 percent of end-2016 common equity at small and medium-sized banks. Raising capital in public markets is complicated, however, by rules against raising capital when price-to-book ratios are below 1. On-balance-sheet shadow credit proxy Unsecured interbank liabilities Net issuance 20 12 :Q 1 12 :Q 2 12 :Q 3 12 :Q 4 13 :Q 1 13 :Q 2 13 :Q 3 13 :Q 4 14 :Q 1 14 :Q 2 14 :Q 3 14 :Q 4 15 :Q 1 15 :Q 2 15 :Q 3 15 :Q 4 16 :Q 1 16 :Q 2 16 :Q 3 16 :Q 4 17 :Q 1 17 :Q 2 –1,500 –1,000 –500 500 1,000 1,500 2,000 2,500 3,000 0 –400 –200 0 200 400 600 800 1,000 1,200 2015 16 17 ... and so did off-balance-sheet shadow credit. 2. Bank-Sponsored Wealth Management Product Net Issuance (Billions of renminbi) Interbank lending and shadow credit dipped sharply in 2017 ... Figure 1.27. China: Regulatory Tightening Has Helped Contain Financial Sector Risks 1. Small and Medium-Sized Banks: Monthly Change in Selected Balance Sheet Categories (Billions of renminbi, three-month average) Sources: CEIC Data Co. Ltd.; China Banking Regulatory Commission; Haver Analytics; media reports; People’s Bank of China; Wind data; and IMF staff calculations. 40 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 part by shifting their business model toward shadow credit activities, which account for a growing share of revenue (Figure 1.29, panel 2) and balance sheets, with shadow products surpassing loan growth over the past three years by a wide margin. A return to traditional lending would strain profits at smaller banks via several channels. Net interest income from loans and deposits fell from 1.7 percent of assets in 2011 to just 1.0 percent in 2016, reflect- ing the changing asset mix but also the higher (and relatively liberalized) interest rates in the shadow credit market (Figure 1.29, panel 2).37 Profitability could suffer if more credit flows through the formal loan market, which is subject to more conservative provisioning rules and macroprudential controls on sector allocation. Any tightening in shadow credit 37The deterioration in net interest margins is mostly attributable to the traditional lending and deposit-taking business, whereas shadow investment and funding activities have had a neutral or posi- tive contribution on a net basis, particularly at smaller lenders. activities would likely crimp net fees and commis- sions, which have doubled since 2011 at smaller banks on the back of higher off-balance-sheet income related to shadow products. Reducing wholesale funding will also weigh on credit growth, particularly at small and medium lenders. These banks have funded much of their growth via nondeposit-funding sources with shorter maturities. Nondeposit funding maturing in less than one year has risen to about 34 percent of assets, from 22 percent in 2011, with over half maturing in less than three months (Figure 1.29, panel 3). The result has been a sharp increase in short-term borrowing to finance long-maturity assets, with short-term nondeposit funding exceeding similar-maturity nonloan assets by about 6 percent of assets, or RMB 2.8 trillion (see Figure 1.29, panel 4). Any meaningful reduction in short-term mar- ket funding would require liquidating longer-term assets. To be successful, regulatory tightening on lend- ers must be accompanied by reforms that reduce 0 5 10 15 2016 (actual) ... or increased capital against 10 percent of shadow credit1 ... with a 25 bps fall in ROA ... No shadow credit growth ... Figure 1.28. Chinese Banks: Financial Policy Tightening and Credit Growth Capacity 1. Realized and Projected Credit Growth Capacity1 (Percent) Curbing shadow credit slows overall credit ... ... and would reduce vulnerabilities only gradually. ... especially if other weaknesses are also addressed ... 3. Shadow Credit to Capital (Percent) 2. Net New Bank Credit, Realized and Projected Capacity (Trillions of renminbi; percent) 2017 total credit growth capacity 11% 8% 7% 0 5 10 15 20 25 30 2014 15 16 172 173 Shadow credit growth assumption 27% 0% Actual credit growth Projected credit growth capacity 27% 0% 0 100 200 300 400 500 600 700 800 2013 14 15 16 17E Small and medium-sized banks Shadow credit growth assumption Big 5 banks 27% 0% Sources: Company annual reports; SNL Financial; and IMF staff calculations. Note: Shadow credit refers to nonbond, nonloan credit to nonfinancial private borrowers, both on and off balance sheet. For a complete definition, please see footnote 33. bps = basis points; E = estimated; ROA = return on assets. 1Credit growth capacity is calculated at the bank level for 32 firms as the maximum net new credit possible given assumptions for growth in shadow credit (on and off balance sheet) and common equity Tier 1 (CET1) capital. Changes in shadow credit affects the CET1 available to support credit growth. New shadow credit is assumed to carry regulatory capital risk weightings of 25 percent, whereas off-balance-sheet shadow credit carries a risk weighting of zero. Assumes firm-level profitability, dividend payout ratio, CET1 ratio, and loan mix from 2016 stay constant. 2Projected credit growth capacity assuming shadow credit growth of 27 percent year over year. 3Projected credit growth capacity assuming shadow credit growth of 0 percent year over year. 41 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 the economy’s vulnerability to slower credit growth. Authorities’ recent efforts to improve banks’ risk man- agement and reduce maturity and liquidity transfor- mation risks in shadow credit activities are necessary and must be deepened. Stability risks will nonetheless remain elevated, however, if banks support continued rapid credit growth: they will have fewer buffers to recognize losses, profitability could compress further at weaker lenders, and incentives for regulatory arbi- trage will remain strong. Raising new equity would allow banks to raise provisions and capital without slowing credit growth, but must be accompanied by reforms to strengthen bank risk management and governance. A broader reform package could help mitigate the economic impact of slower credit growth and tighter regulations while addressing vulnerabilities. On the borrower side, authorities must build on their commitment to reduce corporate leverage, resolve nonviable firms, and improve credit efficiency.38 With lenders, regulation to reduce shadow credit risks and 38IMF 2016b, 2016c, and 2017f discuss progress and recommen- dations on these topics in more detail. Net income (percent of average assets, left scale) Less than three months Three to 12 months Over 12 months Net interest margin (percent, left scale) Provisions (percent of average assets, right scale) Net fee and commission income NII: other interest income minus other interest expense NII: loans and deposits Provision expense Big 5 Small and medium-sized banks Total 1. Selected Profitability Indicators (Percent of average assets, percent) Bank earnings are lower due to narrower margins and rising provisions ... 0.9 1.4 Sources: SNL Financial; and IMF staff calculations. Note: Shadow credit refers to banks’ nonloan, nonbond credit to nonfinancial private borrowers, both on and off balance sheet. For a complete definition, see footnote 33. NII = net interest income. 1Assets and liabilities available on demand or maturing in three months or less. Figure 1.29. Bank Profitability and Liquidity Indicators 2. Small and Medium-Sized Banks: Selected Revenues and Expenses (Percent of assets) ... but would be worse without shadow-related income. –0.75 3.00 0.00 0.75 1.50 2.25 0.9 1.2 1.5 1.8 2.0 2.3 2.6 0.1 0.6 0.2 0.3 0.4 0.5 2011 12 13 14 15 16 1.0 1.1 1.2 1.3 2011 12 13 14 15 16 3. Small and Medium-Sized Banks: Nondeposit Funding by Maturity (Percent of assets) Growing use of risky short-term funding ... 40 2011 12 13 14 15 16 –0.74 Shadow- credit- related income 0.63 0.82 1.05 0.61 0.98 –0.69 1.17 0.53 0.98 –0.50 1.32 0.44 0.87 –0.31 1.42 0.34 0.76 –0.28 1.58 0.31 0.67 –0.30 1.73 0 5 10 15 20 25 30 35 2011 12 13 14 15 16 4. Banks: Short-Term Nondeposit Funding Minus Short-Term Nonloan Assets1 (Percent of assets) ... has led to worsening maturity mismatches. –8 –6 –4 –2 0 2 4 6 8 2011 12 13 14 15 16 42 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 regulatory arbitrage should be further strengthened. Policies should target reducing balance sheet vulner- abilities at weak banks, including through restricting dividend payouts. Restructuring or resolving nonvia- ble financial institutions would also support corporate debt restructuring and strengthen risk management and governance incentives. The forthcoming IMF– World Bank Financial Sector Assessment Program report on China will discuss financial sector stability issues in China in more detail and provide specific recommendations. Could Rising Medium-Term Vulnerabilities Derail the Global Recovery? Concerns about a continuing buildup in debt loads and overstretched asset valuations could have global economic repercussions. This section uses a scenario analysis to illustrate how a repricing of risks could lead to a rise in credit spreads and a fall in capital market and housing prices, derailing the economic recovery and undermining financial stability. This section illustrates how shocks to individual credit and financial markets well within historical norms can propagate and lead to larger global impacts because of knock-on effects, a dearth of policy buf- fers, and extreme starting points in debt levels and asset valuations. A sudden uncoiling of compressed risk premiums, declines in asset prices, and rises in volatility would lead to a global financial downturn. With monetary policy in several advanced economies at or close to the effective lower bound, the economic consequences would be magnified by the limited scope for monetary stimulus. Indeed, monetary policy nor- malization would be stalled in its tracks and reversed in some cases. The Global Macrofinancial Model documented in Vitek 2017 is used to assess the consequences of a continued buildup in debt and an extended rise in risky asset prices, from already elevated levels in some cases. This dynamic stochastic general equilib- rium model covers 40 economies and features exten- sive macro-financial linkages—with both bank- and capital-market-based financial intermediation—as well as diverse spillover channels. This scenario has two phases. The first phase features a continuation of low volatility and com- pressed spreads. Equity and housing prices continue to climb in overheated markets. As collateral values rise, bank lending conditions adjust to maintain steady loan-to-value ratios, facilitating favorable bank lending rates and more credit growth. As discussed, leverage in the nonfinancial private sector has already increased over the past decade across major advanced and emerging market economies. In the scenario, a further loosening in lending conditions, com- bined with low default rates and low volatility, leads investors to drift beyond their traditional risk limits as the search for yield intensifies despite increases in policy rates. As presented earlier, market and credit risk premi- ums are close to decade-low levels—leaving markets exposed to a decompression of risk premiums. Thus, the second phase begins with a rapid decompression of credit spreads and declines of up to 15 and 9 per- cent in equity and house prices, respectively, starting at the beginning of 2020. This shift reflects debt lev- els breaching critical thresholds, prompting markets to grow concerned about debt sustainability, while risk premiums jump, aggravating deleveraging pres- sures. As risk premiums rise, debt servicing pressures are revealed as high debt-to-income ratios make bor- rowers more vulnerable to shocks. The asset repricing is moderate in magnitude, but is broad-based across jurisdictions and leads to a tightening of financial conditions. Flight to quality flows reduce long-term bond yields in safe havens and raise them in the rest of the world. Segments with higher leverage and extended valuations are hit particularly hard, leading to higher funding costs and debt servicing strains. Underlying vulnerabilities are exposed, and the global recovery is interrupted. Figure 1.30 summarizes the main impacts and spillovers: • The global economic impact of this scenario is broad-based and significant, about one-third as severe as the global financial crisis.39 The level of global output falls by 1.7 percent by 2022 relative to the WEO baseline, with varying cross-country impacts. • The severity of the economic impact on the United States is cushioned by stronger bank buffers, milder house price declines, and more monetary policy 39The results are broadly consistent with Chapter 2, which finds that increases in household debt from already elevated levels signal high economic risks, and with Chapter 3, which concludes that rising private sector leverage signals higher downside risks to growth over the medium term. 43 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Euro area United States ChinaOther advanced economies Other emerging market economies Mortgage debt: median country Corporate debt: median country High Medium Low High Medium Low 1. Price of Equity –15 –10 Pe rc en t Pe rc en ta ge p oi nt s Pe rc en t Pe rc en t –5 0 5 10 2017 18 19 20 21 22 2017 18 19 20 21 22 2017 18 19 20 21 22 2017 18 19 20 21 22 3. Nominal Policy Interest Rate –2 –1 0 1 5. Output Losses 6. Reductions in Bank Capital Ratios 4. Mortgage and Corporate Debt 2. Price of Housing –6 –4 –2 0 2 4 –2.0 –0.5 –1.0 –1.5 0.0 0.5 1.0 1.5 Figure 1.30. Global Financial Dislocation Scenario Financial stability risks build up for two more years, as equity and house prices continue to rise amid low volatility and narrow spreads, followed by an eventual sharp repricing. Monetary policy responses are limited by policy space in some countries. A decompression of risk premiums leads to an abrupt deleveraging. Output losses are broad-based. Rising defaults reduce capital at banks. Source: IMF staff estimates. Note: The variables in all panels are expressed as deviations from baseline. In panel 5, countries are shaded according to the following magnitudes of output losses: (1) smaller than 1.8 percent of GDP (“low impact”), (2) between 1.8 percent and 2.3 percent of GDP (“medium impact”), and (3) greater than 2.3 percent of GDP (“high impact”). In panel 6, the thresholds for reductions in bank capital ratios are (1) smaller than 0.625 percentage points (“low impact”), (2) between 0.625 and 0.675 percentage points (“medium impact”), and (3) greater than 0.675 percentage points (“high impact”). 44 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 space compared with other advanced economies, despite relatively high equity valuations. The Federal Reserve reverses interest rate hikes during the second phase of the scenario, cutting the policy rate by 150 basis points to 1.75 percent by 2022. • The euro area suffers a larger output loss because the policy rate is at the effective lower bound and—as a result of renewed financial fragmentation—term premiums rise in high-spread euro area economies. Government debt ratios climb because nominal output is lower and debt service costs are higher for these economies. • Emerging market economies are disproportionately affected by the correction in global risk assets. The flight to quality prompts outflows from their equity and bond markets, putting pressure on curren- cies and challenging countries with large external financing needs. • Corporate and household defaults rise on the back of higher interest costs, lower earnings, and weaker growth. Default rates do not breach global financial crisis levels but return to levels consistent with prior cyclical peaks. Firms in some euro area countries and China with excessive debt overhangs are more sensitive to the increase in credit costs. Household leverage and high house prices in Australia and Canada make these economies more susceptible to risk premium shocks. • Higher credit and trading losses, in turn, reduce bank capital ratios to varying degrees worldwide. Banking systems in advanced economies are health- ier compared with the precrisis period, while lever- age is less of a potential amplifier. Chinese banks suffer outsize declines in capital, but strong policy buffers could be used to mitigate the financial and economic impacts. Emerging Markets Would Suffer a Retrenchment in Foreign Capital Inflows Drawing on the above scenario, the potential for emerging market stress due to pressures on portfo- lio inflows is examined in more detail, including by taking into account the likely reduction in these flows from Federal Reserve balance sheet normalization (as discussed earlier). • During the first phase of the scenario, portfolio flows to emerging market economies are supported by rising investor risk appetite. This partially offsets the drag on portfolio inflows from US monetary policy normalization observed during 2017–19. As a result, there is a (net) reduction in portfolio inflows to emerging market economies of about $25 billion a year, compared with $35 billion under the baseline (Figure 1.31, panel 1). • During the second phase of the scenario, the asset market correction triggers a more rapid retrench- ment in capital inflows to emerging market econ- omies of about $65 billion over the first four quarters, in addition to the projected reduction of $35 billion in inflows associated with continued Federal Reserve balance sheet normalization. The combined effect results in a reduction of portfolio inflows of some $100 billion during the first four quarters of the correction (and about $65 billion during the subsequent four quarters). • At the country level, the associated portfolio inflow reduction during the first two years of the shock to global risk premiums ranges from 1.6 to 2.3 percent of GDP for the most affected countries (Fig- ure 1.31, panel 2). Such a reduction is likely to lead to an outright reversal of portfolio flows, at least during some quarters, considering that the decom- pression of risk premiums is likely to be more rapid in some periods than in others (rather than unfold- ing at a steady pace as depicted in this exercise). The buildup in external financing pressures could be particularly challenging for countries with large and rising projected current account deficits. For example, Colombia, South Africa, and Turkey have projected current account deficits in the range of 3 to 4½ per- cent of GDP in 2019 (Figure 1.31, panel 3). More- over, emerging market currencies would come under pressure, limiting space for monetary policy to ease. In turn, higher domestic interest rates would affect firms’ debt servicing capacity, hitting those with still high lev- els of corporate leverage and increasing risks to weaker banking systems (as explored in the April 2017 GFSR) (Figure 1.31, panel 4). Emerging Market Policies In emerging market economies, policymakers should take advantage of current favorable external conditions to further enhance their resilience, includ- ing by continuing to strengthen external positions where needed and reduce corporate leverage where it is high. Deploying policy buffers and exchange rate flexibility would help buffer external shocks, while 45 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 improving corporate debt-restructuring mechanisms and monitoring firms’ foreign exchange exposures would lower corporate vulnerabilities. Advances in these areas would leave these economies better placed to cushion any reduction in capital inflows that may occur from monetary policy normalization in advanced economies. However, capital outflow pressures could become more significant if there is a severe retrenchment in global risk appetite, as in the scenario described earlier. Such pressures should usually be handled primarily with macroeconomic, structural, and financial policies, although the appropriate response will differ across countries depending on available policy space (see IMF 2012, 2015, 2016a). Where appropriate, exchange rate flexibility should be a key shock absorber, but in countries with sufficient international reserves, foreign exchange intervention can be useful to prevent disor- derly market conditions. In periods of stress, liquidity provision may also be needed to support the orderly 25th percentile 50th percentile 75th percentile Peak Current: below peak 2013 2017 2019 Baseline ScenarioAdditional impact under scenario Impact under baseline Net impact under scenario 1. Estimated Cumulative Reduction in Emerging Market Portfolio Flows (Billions of US dollars) US monetary normalization and a global asset market correction would increase capital outflow pressures. Figure 1.31. Emerging Market Economy External Vulnerabilities and Corporate Leverage 2. Estimated Peak Reduction in Emerging Market Portfolio Flows (Percent of GDP, reduction over four quarters) Countries that previously received large inflows may see sizable outflows. 1.0 4.0 3.0 3.5 2.0 1.5 2.5 4.5 5.0 5.5 6.0 3. Current Account Balances (Percent of GDP) Outflows could be challenging for countries with large current account deficits ... 4. Corporate Leverage (Total debt to EBITDA, multiple) ... and put pressure on those with high levels of corporate leverage. ARG IND MEX ZAF TUR ARG BRA CHN IDN RUS BRA CHN IDN RUS –7 –6 –5 –4 –3 –2 –1 0 1 2 3 4 Tu rk ey So ut h Af ric a Ch ile Co lo m bi a In do ne si a Br az il M ex ic o In di a Po la nd Ru ss ia Ch in a M al ay si a –300 –200 –100 0 100 2017 18 19 20 21 22 –2.5 –2.0 –1.5 –1.0 –0.5 0.0 Ch in a In di a Br az il In do ne si a Tu rk ey Po la nd Co lo m bi a M al ay si a Ch ile M ex ic o So ut h Af ric a Sources: Bank of America Merrill Lynch; Bloomberg Finance L.P.; Capital IQ; Haver Analytics; and IMF staff calculations. Note: Data labels in the figure use International Organization for Standardization (ISO) country codes. EBITDA = earnings before interest, taxes, depreciation, and amortization. 2003 04 05 06 07 08 09 10 11 12 13 14 15 16 46 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 functioning of financial markets. Capital flow manage- ment measures should be implemented only in crisis situations, or when a crisis is considered imminent, and should not substitute for any needed macroeco- nomic adjustment. When circumstances warrant the use of such measures on outflows, they should be transparent, temporary, and nondiscriminatory and should be lifted once crisis conditions abate. 47 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Prolonged monetary accommodation—and a continuing need to sustain economic momentum— has contributed to a widening divergence between financial and economic cycles. Rapid inflation of asset prices has ensued as large output gaps necessitate an unusually protracted period of low interest rates. This asset price growth has been accompanied by gather- ing strength in credit growth and rising leverage, the combination of which has facilitated strong financial expansion across several economies. Such financial expansions have generally been accompanied by less remarkable economic recoveries, leading to only slowly dissipating negative output gaps. This divergence creates a challenge for monetary and financial policies to support economic recovery while ensuring that medium-term risks do not build. • In the United States, a maturing financial cycle expansion has combined with a slowly closing output gap. The combined growth of asset prices (equity, bond, property) since the recent recession has seen one of the longest and largest cyclical expansions since 1970, albeit from a relatively weak starting point (Figure 1.1.1, panel 1). This growth across asset markets has only moderated a little from its peaks, while credit growth has been gathering momentum. This box was prepared by Paul Hiebert, Yingyuan Chen, and Yves Schüler (Deutsche Bundesbank). At the same time, an unusually large negative output gap has been slow to close, suggesting a need for complementary macroeconomic and financial sector policies to support the economic recovery while attenuating the financial cycle upswing as needed. • In the euro area, the divergence between financial and economic cycles is also growing. A strong asset price boom is only slightly off recent peaks, while credit growth is slowly recovering (Figure 1.1.1, panel 2). This contrasts with a persistently large negative output gap—also suggesting a need for continued accommodative macroeconomic policies and tighter financial sector policies, as warranted in particular euro area member countries. • The financial cycle in Japan, in contrast, has been more muted in tandem with a weak economic recovery, while asset price inflation has been volatile and oscillating around long-term trends in recent years. Recently, however, stronger credit growth has emerged along with a narrowing of the negative output gap. • In other economies where debt service ratios for the private nonfinancial sectors have risen to high levels—such as Australia, Brazil, Canada, China, and Korea—there is a particularly strong need for financial sector policy vigilance to guard against any further buildup of imbalances. Box 1.1. A Widening Divergence between Financial and Economic Cycles 48 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 –8 –6 –4 –2 0 2 4 –8 –6 –4 –2 0 2 4 6 0 4 8 12 16 20 24 28 32 36 40 –0.04 –0.03 –0.02 –0.01 0.00 0.01 0.02 0.03 0.04 0 4 8 12 16 20 24 28 32 36 40 2009 11 13 15 17:Q12009 11 13 15 17:Q1 –0.10 –0.08 –0.06 –0.04 –0.02 0.00 0.02 0.04 0.06 0.08 0.10 –0.04 –0.02 0.00 0.02 0.04 0.06 0.08 0.10 –0.04 –0.02 0.00 0.02 0.04 0.06 0.08 0.10 0 4 8 12 16 20 24 28 32 36 40 US Cycles: Current versus Historical since 1970 Asset and Credit Cycles and Output Gap United States Euro area Japan 2009 11 13 15 17:Q1 Maximum of past cycles Minimum of past cycles Current cycle Quarters from start of cycle 1. Asset Cycle (Quarterly index, deviation of filtered real growth from its historical average) 2. Credit Cycle (Quarterly index, deviation of filtered real growth from its historical average) 3. Output Gap (Percentage points) 4. Asset Cycle (Quarterly index, deviation of filtered real growth from its historical average) 5. Credit Cycle (Quarterly index, deviation of filtered real growth from its historical average) 6. Output Gap (Percentage points) The US financial expansion and output gap are noteworthy by historical standards ... ... as a cumulative gap grows between financial and economic cycles across major advanced economies. Sources: Bank for International Settlements; IMF, World Economic Outlook database; national sources; and IMF staff estimates. Note: Cycles are dated using National Bureau of Economic Research recession dates. Cycles capture low-frequency movements around long-term rates. Real asset price cycles combine momentum common to equity, corporate bond, and house price indices—deflated using national consumer price indices. The credit cycle is real total nonfinancial sector credit. For more information on the underlying methodology, see Schüler, Hiebert, and Peltonen 2017. Figure 1.1.1. Financial and Economic Cycles Box 1.1 (continued) 49 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Cyberthreats to financial institutions are growing, and events in 2016 and 2017 have altered the threat landscape substantially. There has been a sizable increase in the impact and sophistication of financially motivated cyberattacks on financial institutions.1 Cyberthreats can be related to financial gain— including malware attacks—or can aim to destroy information technology systems. Some estimates place the economic losses of a hypothetical major global cyberattack as high as $53 billion (Lloyds 2017). While the magnitude and frequency of attacks have grown, their nature has evolved as perpetrators have adopted operational models that replicate legitimate businesses, such as the use of vertically integrated software packages and cloud-based operations. This evolution renders the technology both more potent and easier to access. Moreover, because cyberthreats are international and can become systemic, private sector institutions are not well positioned to respond effec- tively on their own. A coordinated regulatory approach is needed, which would result in a consistent risk mitigation framework to support financial stability. The systemic risk ramifications of a cyberattack could be substantial. There are several channels through which cybersecurity events could threaten financial stability: (1) data breach, (2) disruption of business, (3) integrity attack (modifications to internal data), and (4) malicious activities (financial gain). Greater reliance on technology, combined with the interconnection of the global financial system, means that many, if not all, participants in the system are at risk. Banks and financial market infrastructures, in particular, harbor the potential for contagious cyberrisk, given their interconnection—so that attacks on individual financial institutions can quickly fan out across national financial systems and beyond. A recent example concerns the June 2017 “NotPetya” attack, disguised as ransomware, which among others severely hit bank operations in Ukraine. Information technol- ogy systems in the country, including automatic teller machines, were rendered unusable. Problems spilled across borders2 at a total global cost of some $850 mil- lion. Other interconnected financial institutions, such as financial infrastructures (for example, payment, This box was prepared by Tamas Gaidosch and Chris Wilson. 1For example, the number of stolen identities rose 95 percent year over year in 2016, according to Symantec. 2For example, two multinational companies estimated losses from NotPetya exceeding $130 million each. clearing, and settlement systems), are also at risk. Insurance companies are less exposed through connect- edness; however, their indirect exposure through their cyberinsurance risk underwriting can be significant and is not fully understood.3 A global and coordinated policy response is needed to ensure resilience to cyberattacks and combat cybercrime. Regulators have begun introducing cybersecurity regulations. Among recent initiatives, the European Parliament—following up on the EU-wide Cybersecurity Strategy—adopted the directive on secu- rity of network and information systems; the European Banking Authority issued guidelines on information and communications technology risk assessment; the Bank of England launched a vulnerability testing framework and set out a supervisory statement on cyberinsurance underwriting risk; the Board of Governors of the Federal Reserve System, the Office of the Comptroller of the Currency, and the Federal Deposit Insurance Corporation jointly published a notice of proposed rulemaking regarding enhanced cyberrisk management standards; the Committee on Payments and Market Infrastructures and the Board of the International Organization of Securities Com- missions issued cyberguidance for financial market infrastructures; and the New York State Department of Financial Services issued Cybersecurity Requirements for Financial Services Companies. The EU-wide Gen- eral Data Protection Regulation, effective May 2018, although not specific to the financial sector, will never- theless have a significant global impact on the system, given its extraterritorial applicability and potentially drastic fines for data breaches.4 While regulations converge on common themes, their sectoral applica- bility and level of detail vary, which presents compli- ance difficulties for international operations. Tackling cybercrime effectively means attacking its business model. The risks of being engaged in cybercrime must be raised significantly, underpinned by stronger inter- national coordination. Beyond ensuring resilience, regulation has increas- ingly focused on prevention. Frameworks are being designed for the identification and prevention of cyberincidents, as well as for timely recovery and information sharing. Ongoing initiatives by financial 3As evidenced by the recent supervisory statement of the Bank of England on cyberinsurance underwriting risk. 4Fines can be up to 4 percent of yearly turnover or €20 mil- lion, whichever is greater. Box 1.2. Cyberthreats as a Financial Stability Risk 50 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 regulators typically include practical countermeasures such as requirements on penetration and resilience tests (for example, testing how far into an organiza- tion’s system hackers can go and how well the system defends itself and recovers). As these regulations take hold, harmonization of minimum standards is needed to help smooth implementation, especially for institu- tions operating across borders and sectors. More inter- national coordination would be helpful to share good practice, identify emerging risks, and raise standards across the entire global system—including, as needed, broader cross-border cooperation and information sharing with intelligence and other agencies outside the financial sector, among others. Box 1.2 (continued) 51 C H A P T E R 1 I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 References Adrian, Tobias, and Markus Brunnermeier. 2016. “CoVaR.” American Economic Review 106 (7): 1705–41. Adrian, Tobias, Richard Crump, and Emanuel Moench. 2013. “Pricing the Term Structure with Linear Regressions.” Staff Report 340 (revised), Federal Reserve Bank of New York. Adrian, Tobias, Michael Fleming, Shachar Or, and Erik Vogt. 2017. “Market Liquidity after the Financial Cri- sis.” Staff Report 796 (revised), Federal Reserve Bank of New York, June. Adrian, Tobias, and Hyun Song Shin. 2013. “Procyclical Lever- age and Value-at-Risk.” Staff Report 338 (revised), Federal Reserve Bank of New York. Arslanalp, Serkan and Takahiro Tsuda. 2012. “Tracking Global Demand for Advanced Economy Sovereign Debt”, IMF Working Paper 12/284, Updated, International Monetary Fund, Washington, DC. Bank for International Settlements (BIS). 2012. “Do Debt Service Costs Affect Macroeconomic and Financial Stability?” BIS Quarterly Review (September): 21–35. ———. 2017. Annual Report: The Global Economy: Maturing Recoveries, Turning Financial Cycles? Basel. Bank of England. 2017. “Changing Risks and the Search for Yield on Solvency II Capital.” Speech by David Rule, July 6. Bank Wealth Management Registration and Trusteeship Center. 2017. China Bank Wealth Management Market Annual Report 2016 (in Chinese). http:// www .chinawealth .com .cn/ resource/ 830/ 846/ 863/ 51198/ 52005/ 961636/ 1495184467803885769503 . Basel Committee on Banking Supervision. 2014. “The G-SIB Assessment Methodology—Score Calculation.” Bank for International Settlements, Basel. Bonis, Brian, Jane Ihrig, and Min Wei. 2017. “The Effect of the Federal Reserve’s Securities Holdings on Longer-Term Interest Rates.” FEDS Notes, Board of Governors of the Federal Reserve System. https:// www .federalreserve .gov/ econres/ notes / feds -notes/ effect -of -the -federal -reserves -securities -holdings -on -longer -term -interest -rates -20170420 .htm. Brunnermeier, Markus, and Yuliy Sannikov. 2014. “A Macro- economic Model with a Financial Sector.” American Economic Review 104 (2): 379–421. Caruana, Jaime. 2017. “Have We Passed ‘Peak Finance’?” Inter- national Center for Monetary and Banking Studies, Bank for International Settlements, Basel. Cetorelli, Nicola, and Linda Goldberg. 2012. “Banking Global- ization and Monetary Transmission.” Journal of Finance 67 (5): 1811–43. Chandumont, M. L. 2016. “The Volatility Regime.” Actuary 13 (1): 36–43. Dattels, Peter, Rebecca McCaughrin, Ken Miyajima, and Jaume Puig. 2010. “Can You Map Global Financial Stability?” IMF Working Paper 10/145, International Monetary Fund, Washington, DC. Drehman, M., M. Juselius, and A. Korinek. 2017. “Account- ing for Debt Service: The Painful Legacy of Credit Booms.” BIS Working Paper 645, Bank for International Settle- ments, Basel. European Central Bank (ECB). 2017. “Box 2: Financial Vulnerabilities of Euro Area Households.” Financial Stability Review (May). Fiechter, Jonathan, Inci Otker-Robe, Anna Ilyina, Michael Hsu, Andre Santos, and Jay Surti. 2011. “Subsidiaries or Branches: Does One Size Fit All?” IMF Staff Discussion Note 11/04, International Monetary Fund, Washington, DC. Financial Stability Board (FSB). 2017. “Policy Recommenda- tions to Address Structural Vulnerabilities from Asset Man- agement Activities.” January 12. Geanakoplos, John. 2010. “The Leverage Cycle.” NBER Macro- economics Annual (24). Guscina, A., G. Pedras, and G. Presciuttini. 2014. “First-Time International Bond Issuance—New Opportunities and Emerging Risks.” IMF Working Paper 14/127, International Monetary Fund, Washington, DC. International Monetary Fund (IMF). 2012. “The Liberalization and Management of Capital Flows: An Institutional View.” Washington, DC. ———. 2015. “Managing Capital Outflows: Further Opera- tional Considerations.” IMF Policy Paper, Washington, DC. ———. 2016a. “Capital Flows—Review of Experience with the Institutional View.” IMF Policy Paper, Washington, DC. ———. 2016b. “China—2016 Article IV Consultation.” IMF Country Report 16/270, Washington, DC. ———. 2016c. “China—Selected Issues.” IMF Country Report 17/271, Washington, DC. ———. 2017a. “Macroeconomic Developments and Pros- pects in Low-Income Developing Countries.” Policy Paper, Washington, DC. ———. 2017b. “The Medium-Term Debt Management Strategy: An Assessment of Recent Capacity Building.” Policy Paper 072817, Washington, DC. ———. 2017c. “Euro Area Policies—2017 Article IV Consulta- tion.” IMF Country Report 17/235, Washington, DC. ———. 2017d. “Japan—2017 Article IV Consultation.” IMF Country Report 17/242, Washington, DC. ———. 2017e. “Japan—Financial System Stability Assessment.” IMF Country Report 17/244, Washington, DC. ———. 2017f. “China—2017 Article IV Consultation.” IMF Country Report 17/247, Washington, DC. International Organization of Securities Commissions (IOSCO). 2017. “Consultation on CIS Liquidity Risk Management Recommendations.” July. Koepke, Robin. 2014. “Fed Policy Expectations and Portfolio Flows to Emerging Markets.” Working Paper, Institute of International Finance, Washington, DC. Laeven, Luc, and Fabian Valencia. 2012. “Systemic Banking Crises Database: An Update.” IMF Working Paper 12/163, International Monetary Fund, Washington, DC. 52 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Lloyds. 2017. “Counting the Cost: Cyber Exposure Decoded.” Emerging Risks Report 2017. https:// www .lloyds .com /~/ media/ files/ news -and -insight/ risk -insight/ 2017/ cyence / emerging -risk -report -2017 ---counting -the -cost . McCauley, Robert Neil, Agustín Bénétrix, Patrick McGuire, and Goetz von Peter. 2017. “Financial Deglobalisation in Banking?” Working Paper 650, Bank for International Settlements, Basel. Mian, Atif, and Amir Sufi. 2011. “House Prices, Home Equity-Based Borrowing and the US Household Leverage Crisis.” American Economic Review 101: 2132–56. Reinhardt, Dennis, and Steven Riddiough. 2015. “The Two Faces of Cross-Border Banking Flows.” IMF Economic Review 63 (4): 751–91. Schularick, Moritz, and Alan Taylor. 2012. “Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870–2008.” American Economic Review 102 (2): 1029–61. Schüler, Yves S., Paul P. Hiebert, and Tuomas Peltonen. 2017. “Coherent Financial Cycles for G7 Countries: Why Extend- ing Credit Can Be an Asset.” Working Paper 43, European Systemic Risk Board, Frankfurt am Main. UK Financial Conduct Authority (FCA). 2017. “Illiquid Assets and Open-Ended Investment Funds.” Discussion P DP 17/1. February. US Securities and Exchange Commission (SEC). 2016. “Investment Company Risk Management Program Rules.” October 13. Vitek, Francis. 2017. “Policy, Risk and Spillover Analysis in the World Economy: A Panel Dynamic Stochastic General Equi- librium Approach.” IMF Working Paper 17/89, International Monetary Fund, Washington, DC. Wright, Jonathan. 2011. “Term Premia and Inflation Uncer- tainty: Empirical Evidence from an International Panel Dataset.” American Economic Review 101 (4): 1514–34. Summary A lthough finance is generally believed to contribute to long-term economic growth, recent studies have shown that the growth benefits start declining when aggregate leverage is high. At business cycle frequen- cies, new empirical studies—as well as the recent experience from the global financial crisis—have shown that increases in private sector credit, including household debt, may raise the likelihood of a financial crisis and could lead to lower growth. Globally, household debt has continued to grow in the past decade. This chapter takes a comprehensive look at the relationship between household debt, growth, and financial stability across a sample of 80 advanced and emerging market economies. Besides aggregate macro-level analysis, the chapter also delves into micro-level data on individual household borrowing to shed additional light on how household indebtedness affects growth and stability at the aggregate level. The chapter finds that there is a trade-off between the short-term benefits of rising household debt to growth and its medium-term costs to macroeconomic and financial stability. In the short term, an increase in the house- hold debt-to-GDP ratio is typically associated with higher economic growth and lower unemployment, but the effects are reversed in three to five years. Moreover, higher growth in household debt is associated with a greater probability of banking crises. These adverse effects are stronger when household debt is higher and are therefore more pronounced for advanced than for emerging market economies, where household debt and credit market participation are lower. However, country characteristics and institutions can mitigate the risks associated with rising household debt. Even in countries where household debt is high, the growth-stability trade-off can be significantly mitigated through a combination of sound institutions, regulations, and policies. For example, better financial regulation and supervision, less dependence on external financing, flexible exchange rates, and lower income inequality would attenuate the impact of rising household debt on risks to growth. Overall, policymakers should carefully balance the benefits and risks of household debt over various time hori- zons while harnessing the benefits of financial inclusion and development. HOUSEHOLD DEBT AND FINANCIAL STABILIT Y2CHAPTER 53International Monetary Fund | October 2017 54 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Introduction Considerable attention has been paid to household debt since the global financial crisis as it has continued to grow in a wide range of countries (Figure 2.1). The median household debt-to-GDP ratio among emerging market economies increased from 15 percent in 2008 to 21 percent in 2016, and among advanced economies it increased from 52 percent to 63 percent over the same period. At the same time, in the highest quartile, the household debt-to-GDP ratio fell only slightly from 88 percent to 86 percent in advanced economies and continued to rise from 28 percent to 32 percent in emerging market economies. While this increase reflects to some extent the intended effects of expansionary monetary policy, central banks in various advanced and emerging market economies have recently warned against the financial stability risks of high household debt and high debt-to-income ratios when inflation and wage growth are low (see, for example, Reserve Bank of Australia 2017, Bank of Canada 2017, Bank of England 2017, South African Reserve Bank 2017, and Banco Central de Chile 2017). Household debt and access to credit can help boost demand and build personal wealth, but high indebt- edness can also be a source of financial vulnerability. According to the permanent income hypothesis, higher debt indicates higher expected income. It also allows households to make large investments in housing and education and helps smooth consumption over time. In other words, debt allows households to acquire goods and services now and repay gradually, through higher (anticipated) income. In the long term, higher private sector credit supports economic growth (Beck, Levine, and Loayza 2000) although the precise link between growth and household debt is more elusive (Beck and others 2012). Nonetheless, even if positive in the long term, high household indebtedness can cause significant debt overhang problems when a country unexpectedly faces extreme negative shocks. The experience of the global financial crisis suggests that high household debt can be a source of financial vulnerability and lead to prolonged recessions (Mian and Sufi 2011). Broader cross-country studies also indicate that increases in The authors of this chapter are Nico Valckx (team leader), Adrian Alter, Alan Xiaochen Feng, and Xinze Yao, with contribu- tions from Machiko Narita, Feng Li, and Xiaomeng Lu, under the general guidance of Claudio Raddatz and Dong He. Atif Mian was a consultant for this chapter. Claudia Cohen and Breanne Rajkumar provided editorial assistance. household debt may predict lower future income growth and financial crises in the medium term (Mian, Sufi, and Verner, forthcoming; Jordà, Schularick, and Taylor 2016). As household borrowing increases the economy grows quickly in the short term but becomes highly leveraged. In this situation, a macroeconomic shock may increase unemployment and reduce output in the medium term because of financial disruptions or nominal rigidities (for example, downward wage rigidity, a zero lower bound on interest rates, or fixed exchange rates) that may prevent full adjustment to the shock. The macroeconomic and financial risks arising from increasing household debt may not be equally important across countries at different stages of development and with different financial and institutional characteristics. Emerging market economies may be less prepared to deal with the consequences of a household deleveraging pro- cess because of limited institutional capacity. For exam- 0 25 50 75 100 125 1980 85 90 95 2000 05 10 15 10th–90th percentile 25th–75th percentile Median 0 10 20 30 40 50 1995 99 2003 07 11 15 Source: IMF staff calculations. Note: Panels show the cross-country dispersion of household debt-to-GDP ratios. See Annex 2.1 for sample coverage. Figure 2.1. Household Debt-to-GDP Ratio in Advanced and Emerging Market Economies (Percent) 1. Advanced Economies 2. Emerging Market Economies 55 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 ple, lack of effective personal bankruptcy regimes may prevent households and lenders from efficiently dealing with debt overhang. On the other hand, household debt is lower in emerging market economies than in advanced economies reflecting a higher prevalence of financial fric- tions that reduce households’ access to debt. The balance between more financially and institutionally developed economies’ ability to deal with the consequences of higher household debt and the higher debt resulting from those very characteristics will likely determine the effect of household debt on economic growth and financial stability immediately and over the medium term. This chapter takes a comprehensive look at the relationship between household debt, macroeconomic performance, and financial stability across a broad sam- ple of countries. It largely abstracts from the long-term considerations related to financial inclusion and financial access and focuses instead on the short- to medium-term consequences of household debt increases. It does so using a larger sample of advanced and emerging market economies than hitherto investigated to shed new light on the conditions under which household debt increases are more likely to predict subpar macroeconomic perfor- mance, large economic downturns, and financial crises.1 Furthermore, it also explores micro-level data based on national surveys for selected countries to document a series of stylized facts and the underlying mechanisms behind the aggregate results. Specifically, the chapter aims to answer the following questions: • How strongly is household debt aligned with future GDP growth and consumption? Does the pattern differ between advanced and emerging market economies? Does the relationship depend on the institutional context, such as the terms of household debt contracts and various institutional factors? • At the individual household level, what role do income differences play in household borrowing and consump- tion decisions? Is the household debt-to-income ratio very different across income groups and countries? • How strongly is an increase in household debt asso- ciated with the probability of financial crises? Does household debt represent a neglected crash risk? • What are the implications for macroprudential and other policies? 1See Chapter 3 of the April 2012 World Economic Outlook for an earlier analysis of household debt, Chapter 3 of the April 2011 Global Financial Stability Report for an analysis of housing finance and financial stability, and the October 2016 Fiscal Monitor for an analysis of private versus public sector debt. The main findings are as follows: • On average, an increase in household debt boosts growth in the short term but may give rise to macro- economic and financial stability risks in the medium term. Real GDP initially reacts positively to increases in household debt, as do consumption, employ- ment, and house and bank equity prices. However, after one or two years, the dynamic relationship between debt, GDP, consumption, employment, housing, and bank equity prices turns negative. Higher household debt is associated with a greater probability of a banking crisis, especially when debt is already high, and with greater risk of declines in bank equity prices. • But the negative medium-term consequences of increases in household debt are more pronounced for advanced than for emerging market economies. In the latter, the short-term positive relationships between household debt and GDP growth, consumption, and employ- ment are stronger and the negative medium-term association with these variables is weaker. These rela- tionships are explained by the lower average household debt and credit market participation in emerging mar- kets, which may mean narrower and less costly delever- aging from a macro perspective. Or it may imply less room for overborrowing at the aggregate level in coun- tries where other financial frictions constrain access to debt for a larger share of the population. • Country characteristics and the institutional setting play an important role. These negative medium-term effects are reinforced when household debt is high in countries with more open capital accounts and fixed exchange rates, whose financial systems are less devel- oped, and where transparency and consumer financial protection regulation is absent, quality of supervision is lower, and income inequality is larger. While these characteristics are more prevalent in emerging market economies, the lower initial levels of household debt in this group compensate for their amplifying effect for the average emerging market economy in the sample. Nonetheless, these results show that the overall consequences of household debt increases may vary importantly across countries and can be benefi- cial, even at high levels of debt, when the right mix of policies and institutions is in place. • Lower-income groups tend to be more vulnerable. Household surveys confirm that, within countries, the share of lower-income households in total debt has grown. These households typically have higher 56 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 debt-to-income, higher debt-service-to-income, and higher debt-to-assets ratios, which makes them more vulnerable to adverse shocks than higher-income households. • Macroprudential tools are useful. Macroprudential tools that target credit demand, such as restrictions on loan-to-value and debt-to-income ratios, seem to help constrain the growth in household credit. The remainder of the chapter is organized as follows: The chapter first lays out a conceptual framework for household debt and macro-financial stability. It then describes some general developments in household debt, both from a macro and a micro (disaggregated) perspective. Next, it turns to empirical analysis of financial stability risks posed by household debt and the comovement between household debt, income, and consumption for both advanced and emerging market economies. The findings of the chapter lead to ques- tions about the regulatory framework that influences household debt decisions and risk taking, which are addressed subsequently. The last section concludes and presents relevant policy implications. How Does Household Debt Affect Macroeconomic and Financial Stability? This section discusses some of the key models and mecha- nisms through which changes in household debt affect the macroeconomy and financial stability. First, it reviews some long-term relationships between household debt and growth. Next, it discusses the permanent income theory and some alternative models that yield different effects. Higher financial inclusion and financial development can have positive effects on long-term growth, but the relationship between household debt and long-term growth is more elusive. Extensive literature has docu- mented that financial development and the corresponding increase in private credit by both firms and households lead to higher growth (Levine 1998; Beck and Levine 2004, among others). However, the link between house- hold debt and long-term growth has been more elusive, with earlier papers arguing that the growth consequences of household debt depend on the use of borrowed resources, and more recent evidence finding a weak relationship between household debt and GDP growth.2 2For the earlier papers on the conditional relationship between some proxies of household debt and growth, see Jappelli and Pagano 1994 and De Gregorio 1996. For recent analyses that directly More recently, Arcand, Berkes, and Panizza (2015) and Sahay and others (2015b) find that when private sector debt reaches a certain level, the positive effects on per capita growth start to decline, which they relate to the diversion of resources from productive sectors and to rising financial stability risks when the economy becomes highly leveraged (see Box 2.1 for further discussion and a direct analysis of the long-term relationship between household debt and growth). At the business cycle frequency, the permanent income theory argues that household debt has benefi- cial effects on the macroeconomy and on financial sta- bility. Households that anticipate an increase in future income will increase their debt to smooth their con- sumption or make large investments in nonfinancial assets or education (Friedman 1957; Hall 1978).3 A smoother intertemporal consumption pattern improves household welfare and contributes to macroeconomic stability, while credit and asset markets accommo- date the financing needs of households (Uribe and Schmitt-Grohé 2017). As such, household debt also enhances financial stability. But newer theories and empirical evidence show that the relationship between household debt and macro-financial stability can also be negative. More recent consumption and debt theories relax some of the assumptions of the permanent income model and consider the consequences of borrowing constraints, negative externalities, and behavioral biases.4 These consider measures of household debt finding statistically insignifi- cant relationships to long-term growth, see Beck and others 2012; Angeles 2015; and Sahay and others 2015a. 3In this context, demographics and the distribution of income and debt matter. Younger households that anticipate future income growth would borrow more against their future income (Blundell, Browning, and Meghir 1994). Rajan (2010) and Kumhof, Rancière, and Winant (2015) have argued that increased income and wealth inequality led to the rapid growth of household debt in the United States and eventually to the financial crisis in 2008. Coibion and others (2017) find that, over the period 2001–12, income inequality may have indirectly operated as a screening device for banks, given that they lend less to low-income households in high-inequality regions in the United States. 4Market incompleteness may also play a role in households’ borrowing and saving decisions. Sheedy (2014) argues that financial contracts are typically not contingent on all possible future events. Because households do not have access to insurance against future risks that could affect their ability to repay debt, the bundling together of borrowing and a transfer of risk are inefficient. In the same vein, Deaton (1991), Carroll (1992), and Aiyagari (1994) argue that households may maintain a “buffer stock” of precaution- ary savings to smooth out future consumption. This suggests that debt may have a more limited role for macro-financial stability. 57 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 market imperfections may result in household debt becoming a source of vulnerability, with consequent risks for macro-financial stability. Some of the effects are illustrated in Figure 2.2. More specifically: • Borrowing constraints, leverage, and aggregate demand: If aggregate demand determines the level of output, a contraction in demand by highly indebted households will not always be compensated for by an increase in demand by those that are less indebted, which may lead to a recession (Eggertsson and Krugman 2012; Korinek and Simsek 2016). In this type of model, adverse shocks to highly indebted households, such as a reduction in the value of collateral, trigger borrow- ing constraints that lead to a deleveraging process that may further reduce the value of collateral. The pres- ence of nominal rigidities, such as a zero lower bound for nominal interest rates or nominal wages that can- not adjust downward, amplifies the consequences of these shocks.5 For instance, adverse shocks to house prices (or stock prices) reduce homeowners’ equity in their housing assets (or households’ net wealth, respectively). If sufficiently large, this reduction could trigger large debt defaults and impose further downward pressure on house prices (or stock prices, respectively), leading to a debt deflation spiral (Fisher 1933), as illustrated in Figure 2.2.6 This sequence 5A broad set of macroeconomic models with financial frictions predict that high leverage reduces borrowing capacity and amplifies the impact of negative macroeconomic shocks (Kiyotaki and Moore 1997; Bernanke, Gertler, and Gilchrist 1999; Brunnermeier and San- nikov 2014, among others). Although these models focus on firms instead of household debt, the mechanism applies more broadly and is incorporated into newer studies described in this section. 6Note, however, that household debt defaults can also facilitate adjustment to lower debt levels, because it increases the resources Debt default/ bankruptcy Housing Financial assets Other assets Human capital Debt Mortgages Consumer credit Other liabilities Financial sector Housing/ securities market Assets Liabilities High debt level High debt level Fisher’s debt- deflation: declines in asset prices Bank capitalization is impaired, banks reduce lending Downward price spirals due to collateral constraints Worsened household balance sheets lead to more defaults, bankruptcies Household Sector Debt overhang Real economy Corporate investment and employment Household Sector Initial effect after a negative shock hits highly indebted households (for example, income shock, credit tightening) Second-round effects Initial effect after a negative shock hits highly indebted households (for example, income shock, credit tightening) Second-round effects Income Expense Labor income Capital income Consumption Debt service Other expenses Deleveraging reduces aggregate demand Declines in corporate investment and private employment Declines in household income Households cut back consumption further due to lower income Source: IMF staff. Note: This figure depicts the interactions between household debt, the financial sector, and the real economy. The balance sheet view (panel 1) shows assets and liabilities (debt) at the household level, whereas the cash flow view (panel 2) shows household income and expenses in the form of consumption and debt service. The two main channels through which household debt and consumption interact are deleveraging and debt overhang. Debt overhang may adversely affect aggregate demand through deleveraging or a crowding out of consumption by the debt service burden. Deleveraging can occur through forced or accelerated repayment of debt, reduction in new credit, and increased defaults or personal bankruptcies. From a legal standpoint, default follows from a situation in which assets and income are insufficient to cover debt-servicing costs, and bankruptcy from lack of sufficient assets and income to repay the debt. There may be second-round effects, such as Fisher-type debt-deflation dynamics, that may be caused by downward asset price spirals. Figure 2.2. First- and Second-Round Effects of the Buildup of Household Debt on Financial Stability 1. Balance Sheet View 2. Cash Flow View 58 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 generates negative spillovers. It can cause stress to bank capital and balance sheets and thereby harm the rest of the economy and compromise financial stability. Since, when taking on debt, households do not internalize the potential impact of their decisions on aggregate demand and other households, they borrow too much from a social perspective. Hence, better outcomes could be achieved by ex ante policies that reduce the debt level, or constrain its increases (Korinek and Simsek 2016). • Behavioral biases: Short-sighted households may strongly prefer current consumption over future consumption, or neglect crash risk. Households that value too much current consumption (hyperbolic discounting) tend to postpone saving decisions indefinitely and to contract an excessive amount of revolving debt (Laibson 1997). Overoptimism may also lead households to borrow too much, resulting, for instance, in higher credit card debt (Meier and Sprenger 2010). Consistent with the idea of overoptimism, not only among households but also among market participants, recent evidence shows that credit expansions forecast equity crashes (Baron and Xiong 2017). Households that base their expectations solely on extrapolations from past events, when house prices have been growing, may increase their borrowing during housing booms because they expect their home equity to continue growing (Fuster, Laibson, and Mendel 2010; Shiller 2005).7 Alternatively, households may neglect cer- tain low-probability risks, such as potentially large defaults on mortgages affecting AAA-rated securities exposed to these defaults (Gennaioli, Shleifer, and Vishny 2012). Or they may vary in their optimism about returns on risky assets (Geanakoplos 2010), with optimistic agents borrowing from pessimistic ones to purchase assets that serve as collateral. This process may amplify asset prices and leverage cycles and impair financial stability. Finally, tax treat- ment (interest deductibility) may also play a role in explaining a bias toward debt financing for house- holds, much as it does for firms (IMF 2016b). households have at their disposal to cover non-debt-related expenses and maintain their consumption levels (Elul 2008). Such a financial decelerator mechanism may explain why debt overhang is more costly (as measured by consumption loss) in countries where the cost of debt default is very high. 7Cheng, Raina, and Xiong (2014) find that even real estate professionals (midlevel managers in securitized finance) had overly optimistic beliefs about house prices. To summarize, the exact nature of the relationship between household debt and future growth and financial stability may depend on several factors. The relation- ship may be positive if agents behave in a rational, forward-looking manner and contract debt solely with an eye on future income growth and returns to capital in the absence of financial frictions and binding bor- rowing constraints. However, the relationship between household debt and macro-financial stability may turn negative for the reasons described above. The negative relationship may be more likely when households borrow primarily for nonproductive purposes or experience inad- equate returns on their investment. High debt may bring about sharp adjustments in their consumption pattern— through deleveraging—and affect other parts of the economy. Depending on how well a country can absorb macro-financial stress or on the policies and institutions in place—such as the monetary stance, fiscal space, qual- ity of regulation and supervision, capital account open- ness, and the degree of foreign-currency-denominated loans—some episodes of debt overhang and deleveraging may be absorbed more easily than others, in response to exogenous shocks affecting households. Developments in Household Debt around the World This section shows that household debt levels are higher in advanced economies than in emerging market economies and mainly comprise mortgage debt, while household debt has grown substantially in emerging market economies. Micro-level evidence indicates that lower-income households are less likely to borrow, but those that do tend to have riskier borrowing profiles. Household debt to GDP is higher in advanced economies than in emerging market economies, but there is considerable heterogeneity within each group. On average, in 2016, the household debt-to-GDP ratio reached 63 percent in advanced economies and 21 percent in emerging market economies, reflecting differences in financial depth and inclusion across these groups of countries.8 But even in advanced economies, it ranges from about 30 percent of GDP in Latvia to more than 100 percent of GDP in Australia, Cyprus, Denmark, Switzerland, and the Netherlands (Figure 2.3, panel 1). In some emerging market economies, house- 8In this chapter, household debt comprises loans by households from banks and other financial institutions. In some countries, this also includes nonprofit institutions serving households. 59 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 hold debt remained very low, at less than 10 percent of GDP in 2016, in Argentina, Bangladesh, Egypt, Ghana, Pakistan, the Philippines, and Ukraine, while in others, such as Malaysia, South Africa, and Thai- land, it exceeded 50 percent of GDP. More broadly, the cross-country distribution of the household debt- to-GDP ratio is positively correlated with differences in financial development (Figure 2.3, panel 2). Mortgage debt makes up the bulk of household debt in advanced economies, but less so in emerging market economies. It accounts for more than 50 percent of total household debt in most advanced economies, whereas among emerging market economies it captures one-third or less of total household debt (Figure 2.3, panel 3). Indeed, differences in mortgage debt explain a large fraction of the difference in household debt between emerging market and advanced economies. Although the characteristics of mortgages vary widely across countries and jurisdictions, a survey of IMF country desks finds that most mortgages are recourse loans: after a default the lender can try to seize additional household assets to cover the debt if the market value of the house is insufficient (see Annex Figure 2.1.1). Other debt consists primarily of consumer credit, which is typically used to smooth out short-term fluctuations in consumption and income but can also be used to finance microenterprises.9 Household debt has grown substantially in many countries over the past decade and has kept growing in recent years, especially among emerging market economies. Household debt-to-GDP levels fell in the United States and the United Kingdom after the global financial crisis of 2007–08 and in various European countries—most notably, Iceland, Ireland, Portugal, Spain, and the Baltics—in the wake of the European sovereign debt crisis (Figure 2.3, panel 1). In Germany, household debt has fallen as a percentage of GDP since 2000. Notwithstanding these recent declines, the level of household debt to GDP remains high by historical standards in most of these countries and has kept grow- ing in other advanced economies, such as Australia and Canada (Figure 2.3, panel 5). In a number of emerg- ing market economies—most notably Chile, China, Malaysia, Thailand, Paraguay, Poland, and some central and southeastern European countries, household debt to GDP expanded rapidly over a short time, from as low 9For instance, urban Indian households report about one-fifth of their debt to be for business-related purposes. In addition, rural households use two-fifths of their debt for productive purposes, with the highest share among the wealthier households (see Badarinza, Balasubramaniam, and Ramadorai 2016). as 10 percent of GDP in 2005 to more than 60 per- cent of GDP in some cases. This is also reflected in the rapid rise of median household debt-to-GDP ratios in emerging market regions: from between 5 percent and 10 percent in 2000 to between 17 percent and 22 per- cent in 2016 (Figure 2.3, panels 5 and 6). Changes in household debt ratios are driven mainly by debt increases rather than low or negative income growth. In theory, the household debt-to-GDP ratio may go up if debt increases more, or declines less, than GDP does. The rapid rise in the household debt-to-GDP ratio from 1990 to 2007 is due mainly to rapid increases in inflation-adjusted household debt, in both advanced and emerging market economies, amounting to 6.7 percent and 13.4 percent a year, respectively—far exceeding the growth of real GDP and real disposable income (Figure 2.3, panel 4). This rise was facilitated by the sharp decline in interest rates and easier and more widespread access to credit. Hence, debt servicing may not have risen that much. During this period, net wealth also rose on account of strong real house price increases. After 2008, the growth in house- hold debt slowed to 2 percent a year in advanced econ- omies, reflecting a retrenchment of households in the wake of the global financial crisis, and to 6.6 percent a year in emerging market economies. In both cases, debt continued to exceed the rate of GDP growth, leading to increases in the ratio of household debt to GDP. The overall trend in household debt to GDP is very similar to that of the debt-to-assets ratio. For a subsam- ple of 18 Organisation for Economic Co-operation and Development countries, increases in household debt to assets are highly correlated with household debt-to-GDP ratios (Figure 2.4, panel 6). Thus, increases in debt are usually accompanied by rising leverage, meaning that a focus on net wealth may mask underlying vulnerabilities that arise from procyclical asset values. The trend is most notable for mortgage debt—which constitutes the bulk of household debt in many countries—for which there is large comovement with the housing market cycle. As a result, households are less able to tap into their housing wealth to smooth consumption after a shock. Therefore, following the recent empirical literature and without losing much generality, the rest of the empirical analysis focuses on the debt-to-GDP ratio.10 10In the ensuing analysis, using the debt-to-assets ratio instead of the debt-to-GDP ratio for a subset of 26 Organisation for Economic Co-operation and Development countries for which such data are available yields qualitatively the same results (see Figure 2.6, panel 2). 60 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 0 50 100 150 200 0 20 40 60 80 100 120 140 20 16 2007 EMEs AEs 0 30 60 90 120 150 H ou se ho ld d eb t- to -G D P ra tio H ou se ho ld d eb t- to -G D P ra tio Financial development (index) 0 50 100 150 200 Share of mortgage debt EMEs AEs 0 5 10 15 G D P In co m e RH H D H H D /G D P G D P In co m e RH H D H H D /G D P 1990–2007 2008–16 EMEs AEs 0 20 40 60 80 100 1980 85 90 95 2000 05 10 15 Euro area Other AEs CEEC 0 5 10 15 20 25 1995 2000 05 10 15 Asia Africa Middle East Latin America Sources: Bank for International Settlements; CEIC Data Co. Ltd.; Economic Cycle Research Institute; Haver Analytics; IMF, International Financial Statistics, Monetary and Financial Statistics, and World Economic Outlook databases; Jordà-Schularick-Taylor Macrohistory Database; Svirydzenka 2016; Thomson Reuters Datastream; and IMF staff calculations. Note: For countries included in regional breakdowns, see Annex 2.1. In panel 2, financial development is the index taken from Svirydzenka 2016. Panel 4 reports median annual growth rates for each country group and period for real GDP, real disposable household income, real household debt (RHHD), and household debt-to-GDP ratio (HHD/GDP). Dashed line in panel 1 denotes the 45-degree line. AEs = advanced economies; CEEC = Central and Eastern European countries; EMEs = emerging market economies; income = real disposable household income. Figure 2.3. Growth and Composition of Household Debt by Region (Percent) 1. Household Debt-to-GDP Ratio, 2007 and 2016 2. Household Debt-to-GDP Ratio and Financial Development, 2013 3. Household Debt-to-GDP Ratio and Mortgage Share of Debt, 2016 4. Decomposition of Annual Changes in Household Debt Ratio 5. Advanced Economies and Central and Eastern European Countries: Median Household Debt-to-GDP Ratio 6. Emerging Market Economies in Asia, Africa, the Middle East, and Latin America: Median Household Debt-to-GDP Ratio 0 0.2 0.4 0.6 0.8 1.0 0 20 40 60 80 100 61 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 0 20 40 60 80 Q1 Q2 Q3 Income quintile Income quintile Q4 Q5 0 400 800 1,200 Q1 Q2 Q3 Q4 Q5 0 10 20 30 40 50 7 8 9 10 11 12 Pa rt ic ip at io n ra te log (Real GDP per capita) 0 20 40 60 80 0 10 20 30 40 50 M or tg ag e pa rt ic ip at io n ra te Overall participation rate Q1 Q5 0 100 200 300 400 0 50 100 150 M ed ia n de bt -t o- in co m e ra tio Household debt-to-GDP ratio AEs EMEs 0 10 20 30 30 40 50 60 70 80 90 1995 2000 05 10 15 Household debt-to-GDP ratio (left scale) Household debt-to-assets ratio (right scale) Sources: Bank for International Settlements; country panel surveys; Euro Area Housing Finance Network; Luxembourg Wealth Study; Organisation for Economic Co-operation and Development (OECD); US Survey of Consumer Finance; and IMF staff calculations. Note: Panels 1 and 2 show the cross-country dispersion across income quintiles, evaluated at the median for mortgage borrowers (quintile 1 to quintile 5, from lowest to highest income). Dashed lines in panels 4 and 5 denote the 45-degree line. For country coverage, see Annex 2.1. Panel 6 shows debt, asset, and wealth ratios for a subsample of 18 OECD countries for which such data are available since 1995. AEs = advanced economies; EMEs = emerging market economies. Figure 2.4. Household Debt: Evidence from Cross-Country Panel Data (Percent, unless noted otherwise) 1. Loan Participation Rate, 2010 2. Debt-to-Income Ratio, 2010 3. Loan Participation versus per Capita GDP, 2013 (X axis = US dollars purchasing power parity) 4. Mortgage Participation Rate and Overall Participation Rate, 2013 5. Median Debt-to-Income Ratio and Household Debt-to-GDP Ratio, 2013 6. Household Debt-to-GDP Ratio and Debt-to-Assets Ratio 62 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Lower-income groups typically participate less in credit markets, and their credit profiles are weaker. Household survey data from 25 countries show that households in the lowest income quintiles participate much less in mortgage (and overall) credit markets (Figure 2.4, panel 1). Those that do, however, have, on average, higher risk profiles, with higher debt-to-assets and debt-to-income ratios as well as higher debt service ratios (defined as total debt repayment as a percentage of total income) (Figure 2.4, panel 2). This suggests that lower-income households are most vulnerable to cyclical fluctuations in income and are less likely to benefit from positive wealth effects, given their rela- tively low net asset holdings. From a bank’s perspective, these customers generally represent a higher credit risk, which, in turn, may explain the relatively low participa- tion rate, indicating the presence of credit constraints. Differences in participation across countries explain part of the differences in debt ratios between advanced and emerging market economies. As with other measures of financial inclusion, household credit participation increases with economic development, as measured by real GDP per capita (Figure 2.4, panel 3).11 As credit participation increases, it initially covers mainly high-income families and then moves more aggressively toward easing access for lower-income families, as reflected by the curvature of the respective income groups’ lines (Figure 2.4, panel 4). Thus, high credit participation by low-income families is mainly an advanced economy phenomenon; lower-income countries grant access to credit mainly to higher-income households. Since not all households have debt and since debt-to-income ratios vary significantly across households, macro-level measures of household debt (such as debt-to-GDP and debt-to-net-wealth ratios) underestimate the true burden of indebted households (Figure 2.4, panel 5).12 This underestimation could be especially relevant for emerging market economies where participation rates are low and where low macro-level indebtedness may coexist with significant micro-level household indebtedness (see Box 2.2 for an analysis of Chinese households). 11See also Demirgüç-Kunt and Klapper (2012), who find that account penetration is higher in economies with higher national income, as measured by GDP per capita. 12The aggregate measures of household indebtedness correspond to an income-weighted average of individual household debt ratios. Households with no debt but positive income, as well as differences in indebtedness across households, lead to differences between aggre- gate and micro-level measures. The dynamics of household debt are linked to the evolution of house prices. For example, household debt in Canada and the United States evolved very similarly until the global financial crisis (Box 2.3). After the crisis, household debt continued to rise in Canada but fell in the United States as house prices followed different paths: declining in the United States while continuing to appreciate in Canada. As a result, US households’ lever- age for mortgage holders, reflected in the debt-to-income ratio, remained broadly constant, while Canadian mortgage borrowers’ debt to income increased across all income groups and is now much higher than for US households. These patterns suggest that household debt and housing prices have common dynamics (Box 2.4). Similarly, in China, where house prices rose by 16 per- cent in real terms, the debt-to-income ratio increased across most income groups between 2011 and 2015, and especially for lower-income households (Box 2.2). Financial Stability Risks of Household Debt: Empirical Analysis Increases in household debt have a positive short-term but a negative medium-term relationship to macroeconomic aggregates such as GDP growth, consumption, and employ- ment. They also predict downside risks to GDP growth and a higher probability of a banking crisis. However, the strength of the negative association depends on the level of household debt to GDP, getting stronger when this level exceeds certain thresholds. The short-term positive effects are generally stronger and the medium-term negative effects are consistently weaker for emerging market economies. Household Debt and Growth, Consumption, and Employment When household debt increases, future GDP growth and consumption decline and unemployment rises relative to their average values. Changes in household debt have a positive contemporaneous relationship to real GDP growth and a negative association with future real GDP growth, in line with various recent empirical studies.13 Specifically, a 5 percent increase in household debt to GDP over a three-year period forecasts a 1¼ percent decline in real GDP growth three years ahead (Figure 2.5, panel 1).14 These results do not seem to be 13See, for instance, Mian, Sufi, and Verner, forthcoming; Jordà, Schularick, and Taylor 2016; and Lombardi, Mohanty, and Shim 2017. 14The empirical model includes country fixed effects, so that all variables can be interpreted as deviations from their sample averages. 63 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 driven by potential endogeneity concerns.15 A further breakdown shows that household debt is correlated with future declines in private consumption (Fig- ure 2.5, panel 2) but less so with government consump- tion and investment. It is also negatively correlated with the current account deficit. These findings suggests that household debt booms finance consumption expan- sions, often through current account deficits that revert later when consumption and GDP growth also decline. Increases in household debt are also associated with significantly higher unemployment up to four years in the future (Figure 2.5, panel 3). The short-term positive association between changes in household debt and GDP growth is stronger and the medium-term negative relationship weaker for emerg- ing market economies than for advanced economies (Figure 2.5, panel 1). On the other hand, consumption expands less in the short term and declines less in the medium term after household debt increases in emerg- ing market economies (Figure 2.5, panel 2), while the results for unemployment follow a similar pattern as those for GDP (Figure 2.5, panel 3). This suggests that the trade-off between the benefits of increased household participation in credit markets and the risks to macro- economic stability is less striking for these countries, most likely because of lower average household debt, although institutions and policies may also play an important role, as discussed later. Moreover, the evidence on long-term growth reviewed in Box 2.1 suggests that, in the long term, increases in household debt appear positively related to growth up to a certain level.16 Increases in household debt are associated with height- ened downside risks to future GDP growth for all coun- tries, but in emerging market economies they also predict 15Results obtained using instrumental variables yield qualitatively similar and quantitatively larger estimates than those obtained through ordinary least squares. In these estimations, changes in household and firm debt-to-GDP ratios were instrumented by the interaction between a country’s degree of capital account openness and US financial conditions and global liquidity (broad money). Micro-level regressions discussed below—which are much less likely to be affected by potential endogeneity—provide additional support for the causal interpretation of these results. 16The cumulative effect of an increase in household debt on growth, consumption, and employment, inferred from Figure 2.5, is negative in advanced economies and neutral to marginally negative in emerging market economies. However, such an exercise implicitly relates changes in household debt to longer-term growth outcomes, which is more adequately addressed in the framework reviewed in Box 2.1. According to those results, an increase in the household debt-to-GDP ratio raises long-term growth as long as the final ratio is below a threshold between 36 and 70 percent of GDP (corre- sponding to a 90 percent confidence interval). higher upside risks. Quantile regression results show that changes in household debt have important implications for movements in the distribution of future GDP growth (Figure 2.5, panel 4). Initially, household debt is associ- ated with strong positive output growth (the right tail of the distribution), especially among emerging market economies. But three to five years ahead, increases in household debt seem to have a clearer association with below-average movements of future growth (the left tail of the distribution of future real GDP growth).17 This pattern is consistent with the deleveraging and aggregate demand externalities that arise after a period of rapid growth in household debt, resulting in a volume of borrowing above the socially optimal level that leads to important corrections after a shock. It is interesting to note that, among emerging market economies, increases in household debt are associated with worse negative and stronger positive future growth outcomes compared with advanced economies. This finding may reflect the more extreme historical experiences in this group of coun- tries; they benefit more from financial development and improved access to finance but also suffer more strongly during episodes of debt overhang and financial crises. Supply-driven increases in household debt are more damaging to future growth. Using changes in financial conditions to identify supply- and demand-driven increases in household debt, similar to Mian, Sufi, and Verner, forthcoming, shows that the supply-driven component of household debt has a stronger impact on future GDP growth than the demand component (Figure 2.5, panel 5). Similarly, a monetary policy loosening (negative Taylor rule residuals) reinforces the negative relationship between household debt and future economic activity. The negative medium-term association between GDP growth and growing household debt is largely absent at low levels of debt to GDP. At very low levels of house- hold debt to GDP, below 10 percent, the association between increases in debt and future real GDP growth is positive; it turns negative when household indebted- ness exceeds 30 percent of GDP (Figure 2.5, panel 6). Beyond that point, the correlation declines slightly, but it maintains its negative sign. The presence of this nonlin- earity is consistent with recent findings of a bell-shaped 17In advanced economies, an increase in household debt is neg- ative for medium-term GDP growth across the entire distribution of future GDP growth (all quantiles), whereas in emerging market economies, the impact of household debt on future GDP growth is negative only in the left tail of the distribution (when future growth is below average). 64 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 –0.3 –0.2 –0.1 0.1 0.0 0.0 0.0 0.2 t t + 1 t + 2 t + 3 t + 4 t + 5 t + 6 All AEs EMEs –0.3 –0.2 –0.1 0.1 0.2 0.3 t t + 1 t + 2 t + 3 t + 4 t + 5 t + 6 All AEs EMEs –0.1 0.1 0.2 t t + 1 t + 2 t + 3 t + 4 t + 5 t + 6 t t + 1 t + 2 t + 3 t + 4 t + 5 t + 6 All AEs EMEs –0.2 –0.1 0.1 0.0 0.0 0.2 0.3 0. 15 0. 50 0. 85 0. 15 0. 50 0. 85 0. 15 0. 50 0. 85 0. 15 0. 50 0. 85 0. 15 0. 50 0. 85 0. 15 0. 50 0. 85 0. 15 0. 50 0. 85 0. 15 0. 50 0. 85 0. 15 0. 50 0. 85 All AEs EMEs All AEs EMEs All AEs EMEs t t + 2 Percent t + 4 –0.5 –0.4 –0.3 –0.2 –0.1 Joint Supply Demand –0.5 –0.3 –0.1 0.1 0.0 0.3 0.5 <10 10 20 30 40 50 60 70 80 90 100 Sources: Bank for International Settlements; CEIC Data Co. Ltd.; Economic Cycle Research Institute; Haver Analytics; IMF, World Economic Outlook database; Jordà-Schularick-Taylor Macrohistory Database; Penn World Table; and IMF staff calculations. Note: Panels 1, 2, and 3 are from panel regressions of rolling three-year real GDP growth (consumption and unemployment, respectively) up to six years ahead, on lagged changes in household and corporate debt-to-GDP ratios (over a three-year period), controlling for lags of the dependent variable, and country and time fixed effects. Panel 4 shows quantile regression coefficient estimates for changes in the household debt ratio, using the same specification as the panel regression model. Panel 5 breaks down changes in household debt-to-GDP ratios into supply and demand factors, where local financial conditions are assumed to signal supply-side factors, and the residual to reflect other (demand) factors. Panel 6 shows coefficient estimates from a panel regression estimation, conditioning the effect on changes in household debt, and interacted with various debt thresholds. Colored bars indicate that the effects are statistically significant at the 10 percent level or higher. See Annex 2.2 for details of the estimation methodology. AEs = advanced economies; EMEs = emerging market economies. Figure 2.5. Effects of Household Debt on GDP Growth and Consumption 1. Impact on Real GDP Growth (Regression coefficients) 2. Impact on Real Consumption Growth (Regression coefficients) 3. Impact on Unemployment (Regression coefficients) 4. Quantile Regression of Real GDP Growth (Regression coefficients, 15th, 50th, and 85th quantiles) 5. Demand and Supply Effects (Regression coefficients) 6. Real GDP Growth Threshold Effects (Regression coefficients at various household debt-to-GDP levels) 65 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 relationship between financial deepening and long-term growth (Sahay and others 2015b) and studies relating this to increased financial risks (see also Box 2.1). While the threshold above which increases in household debt more strongly signal risks to real activity is low, it is gen- erally above the levels reached by emerging markets in this sample. This finding may partly explain the milder association estimated for this group of countries. The relationship between future GDP growth and household debt is driven mostly by mortgage debt. The finding that the mortgage debt component is statistically significant and the nonmortgage component is not (Fig- ure 2.6, panel 1) goes somewhat against the argument that increases in debt accompanied by a simultaneous accumulation of assets are less risky, because households may be able to tap into these assets when facing shocks. This could be due to the procyclicality of home equity lines or—more generally—to wealth effects that lead households to cut consumption when the value of their housing assets decline.18 Further evidence confirms that the accumulation of assets does not dampen the consequences of increased indebtedness. Changes in the household debt-to-total-assets ratio are associated with growth declines only at horizons beyond five years ahead, with increases in household debt to GDP remaining significant at shorter horizons (Figure 2.6, panel 2). These results suggest that, at business cycle fre- quencies, it is primarily households’ debt service capac- ity, approximated by a higher debt-to-GDP ratio, that signals vulnerabilities rather than their solvency position. Similar results are found in micro-level data: high debt-to-income ratios make households more vulnerable to income shocks. Micro longitudinal data for five euro area countries show that high household indebtedness in 2010, right before the European sovereign debt crisis, caused a significant reduction in consumption between 2010 and 2014 (Figure 2.7, panel 1).19 Furthermore, consumption declined more for the most indebted 18Boom-bust cycles in housing prices that accompany increases in household debt could be driving the results reported above, but further analysis shows that lagged house price growth is not very sig- nificant in growth forecasting regressions. Additional evidence from dynamic panel vector autoregression techniques shows that house price shocks are associated with a gradual rise in household debt, whereas household debt shocks lead to significant increases in house prices in the short term, up to two to three years, but are followed by a fall in house prices afterward (Box 2.5). 19The macroeconomic and unexpected nature of the shock makes it unlikely that the results are driven by the reverse causality argu- ment that individual households borrowed preemptively to hoard liquidity and smooth consumption. –0.3 –0.2 –0.1 0.1 0.0 0.0 Mortgage Nonmortgage –1.0 –0.8 –1.3 –0.5 –0.3 0.3 0.5 Household debt-to-GDP ratio Debt-to-assets ratio Sources: Bank for International Settlements; CEIC Data Co. Ltd.; Economic Cycle Research Institute; Haver Analytics; IMF, World Economic Outlook database; Jordà-Schularick-Taylor Macrohistory Database; Penn World Table; and IMF staff calculations. Note: This figure shows coefficients of household debt variables in panel regressions of real GDP growth, one to six years ahead, on lagged changes in household and corporate debt-to-GDP ratios (over a three-year period), controlling for lags of the dependent variable, and country and time fixed effects. Panel 1 splits household debt into mortgage and nonmortgage debt-to-GDP ratio. Panel 2 includes changes in the household debt-to-GDP ratio and changes in the household debt-to-assets ratio in the panel regression. Estimations are performed over subsamples for which data are available compared with analysis in Figure 2.5. Colored bars indicate that the effects are statistically significant at the 10 percent level or higher. Figure 2.6. Effects of Household Debt on GDP Growth: Robustness Tests (Regression coefficients) t t + 1 t + 2 t + 3 t + 4 t + 5 t + 6 t t + 1 t + 2 t + 3 t + 4 t + 5 t + 6 1. Mortgage and Nonmortgage Debt 2. Debt-to-Assets and Household Debt-to-GDP Ratios 66 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 households (Figure 2.7, panel 2), which also perceived themselves to be the most financially constrained (Fig- ure 2.7, panel 3). The larger reduction in consumption by highly indebted households at the micro level and the corresponding decline in aggregate consumption observed in macro data are consistent with the effects of aggregate demand externalities arising from delever- aging. Evidence for China also shows that consumption of households with high debt-to-income ratios responds more strongly to income shocks (Figure 2.7, panel 4 and Box 2.2). Hence, highly indebted households’ higher marginal propensity to consume may amplify the effect of negative income or credit shocks on China’s econ- omy, in line with evidence in advanced economies (for example, Mian, Rao, and Sufi 2013). Similar results are found for advanced economies, such as Australia, although they are less pronounced. Financial Stability Risks and Neglected Crash Risk Increases in household debt are also good early warning indicators for banking crises.20 A simple look at the data shows that increases in household debt peak about three years before the onset of a banking crisis (Figure 2.8, panel 1). Formal evidence from a logit 20Previous research documenting similar findings includes Gourin- chas and Obstfeld 2012; Drehmann and Tsatsaronis 2014; and Jordà, Schularick, and Taylor 2016. –100 –50 0 50 100 0 500 1,000 1,500 2,000 2,500 Ch an ge in c on su m pt io n (p er ce nt o f i nc om e) Ch an ge in c on su m pt io n (p er ce nt o f i nc om e) Past debt-to-income ratio –4 –3 –2 –1 0 <1 1–3 3–5 >5
Past indebtedness (debt-to-income ratio)
0
5
10
15
0–1 1–2 2–3 3–4 4–5 5–6 >6
Debt-to-income ratio (mortgage debt)
0
5
10
15
20
DTI = 1 DTI = 5 DTI = 1 DTI = 5
China Australia
Sources: European Central Bank Household Finance and Consumption Survey; Household, Income and Labour Dynamics in Australia Survey; China Household
Finance Survey; and IMF staff calculations.
Note: Panels 1–3 present data from euro area countries with a panel dimension (Belgium, Cyprus, Germany, Malta, Netherlands). The change in consumption-to-
income ratio is computed over 2010–14. For panel 4, see Boxes 2.2 and 2.4 for additional information. DTI = debt-to-income ratio.
1In panel 4, results are based on data for households tracked between 2013 and 2015 for China, and between 2006 and 2015 for Australia.
Figure 2.7. Micro-Level Evidence Corroborating the Macro Impact
1. Euro Area: Initial Debt-to-Income Ratio and Changes in
Consumption, 2010–14
2. Euro Area: Drop in Consumption among Indebted
Households, 2010–14
(Percent of income)
3. Homeowners Not Applying for Loans Due to Perceived
Credit Constraint, 2014
(Percent)
4. China and Australia: Response of Consumption to Income
Shocks1
(Percent)

67
C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y
International Monetary Fund | October 2017
panel data model shows that a rise in the household
debt-to-GDP ratio contributes to a greater probability
of banking crises three years ahead (Figure 2.8, panel 2).
The marginal effect, at about 1 percent, is economically
significant, since the unconditional crisis probability is
about 3.5 percent for the countries under examination.
The relationship between increasing household debt
and financial crises is more pronounced when house-
hold debt is high (65 percent of GDP). This is broadly
consistent with the nonlinear effects found for the
relationship between household debt and GDP growth,
with the higher threshold resulting from the extreme
nature of crises as compared with episodes of growth
declines. The existence of nonlinear effects suggests that
debt increases in already highly indebted households
may be hard to sustain when facing a negative income
shock, leading them to drastically reduce consumption
and default on their debts.
Increases in the household debt ratio predict negative
equity excess returns (over the risk-free rate), especially
for the banking sector. Such predictability is present for
both the banking sector and the overall stock market
index (Figure 2.9, panel 1). This negative correlation
may reflect investor overoptimism and a systematic
neglect of the risk of equity crashes (so-called neglected
crash risk) during periods of high growth in household
debt (Figure 2.9, panel 2). Further analysis with quantile
regressions shows that the negative association between
increases in household debt and future equity returns
is stronger in the lower tail of the return distribution
than in the upper tail, confirming that investors appear
to systematically neglect the risk of equity crashes.
Although the neglected crash risk affects all sectors,
predictability is stronger for bank stock returns, suggest-
ing that rising household debt is often associated with
neglected banking sector vulnerabilities.21 As discussed
later in the chapter and shown earlier, these vulner-
abilities may arise both from the ensuing decline in
growth associated with the deleveraging process or from
higher debt defaults from overindebted households.
The predicted decline in overall stock market returns
suggests that growth contractions explain part of these
results. But consistent with a simultaneous role for
21Risk-adjusted abnormal returns of the banking sector are com-
puted to measure the performance of bank stocks relative to market
returns. Abnormal returns are defined as the capital asset pricing
model regression residuals with quarterly data. For each country, the
coefficient on market excess return, that is, the market beta, is esti-
mated in each year based on past return data to avoid using future
information that is unknown in that year.
–5
0
5
10
–4 –3 –2 –1 0 +1 +2 +3 +4 Average
Household debt Corporate debt
0
0.5
1.0
1.5
2.0
Change in corporate
debt
Change in household
debt
Change in household
debt × high household
debt level
1. Increase in Household and Corporate Debt Ratios around
Banking Crises
(Percent)
2. Probability of a Banking Crisis: Marginal Effects
(Percentage points)
Sources: Bank for International Settlements; CEIC Data Co. Ltd.; Economic Cycle
Research Institute; Haver Analytics; IMF, International Financial Statistics, and
Monetary and Financial Statistics databases; Jordà-Schularick-Taylor Macrohistory
Database; Laeven and Valencia 2013; Thomson Reuters Datastream; and IMF staff
calculations.
Note: Panel 1 shows the average growth in ratios of household and nonfinancial
corporate debt to GDP before and after a banking crisis, as well as the uncondi-
tional average growth rate. Panel 2 shows the marginal effects of a panel logit
model for banking crises for 34 countries, with country fixed effects, levels, and
changes in ratios of household and nonfinancial corporate debt to GDP. It also
shows the interaction effect with a high household debt dummy variable, set at 65
percent of GDP, representing the top quintile of the distribution. The effects are
significant at the 10 percent confidence level. Banking crises are taken from the
updated database by Laeven and Valencia (2013).
Figure 2.8. Banking Crises and the Role of Household Debt

68
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International Monetary Fund | October 2017
rising defaults, increases in the household debt ratio are
often associated with higher growth of nonperforming
loans in the country’s banking sector three years later,
confirming that rapid growth in household debt is asso-
ciated with greater banking stress in the future.
When Is Household Debt More Likely to Predict
Low GDP Growth?
The consequences of an increase in household debt
for future growth differ substantially across countries.
The estimated debt-to-GDP-growth relationship exhibits
substantial heterogeneity within both advanced and
emerging market economies (Figure 2.10, panel 1). The
median coefficient for the three-year-ahead impact of an
increase in debt on GDP growth is –0.5 for advanced
economies and –0.13 for emerging market economies.
Within each group of countries, the dispersion of the
estimated coefficients is large, although more so for
emerging market economies, which also have a larger
share of positive country-level coefficients. This dis-
persion suggests that, in addition to the initial level of
household debt documented earlier, country-specific
and institutional factors may play a role in mediating
the relationship between rising household debt and
future economic activity. To investigate the role of
various leading factors, separate panel regressions add
interactions between household debt and a number of
institutional and country-specific characteristics to the
panel regression between changes in household debt and
three-year-ahead GDP growth (Figure 2.10, panel 2).22
Having an open capital account and a fixed exchange
rate regime increases the risks associated with rising
household debt. An open capital account has multiple
benefits for financial integration and access to foreign
capital (Mussa and others 1998; Stulz 1999), but it
also exposes countries experiencing large capital inflows
to sudden stops (Calvo and Reinhart 2000). In this
sample, a more open capital account results in a stronger
negative association between increases in household debt
and future GDP growth.23 This result might arise from
the accumulation of foreign-currency-denominated debt,
similar to findings by Mian, Sufi, and Verner (forthcom-
ing). As noted in the literature, capital flows that sustain
episodes of foreign debt accumulation are frequently
followed by sudden stops that force strong corrections
in consumption, particularly in emerging markets. This
pattern is consistent with a larger differential effect of
capital account openness in this group of economies.
Along similar lines, having a fixed exchange rate regime
reduces an economy’s flexibility to accommodate exter-
nal shocks, resulting in a larger contraction in aggregate
demand, especially in the presence of nominal wage
rigidities (Schmitt-Grohé and Uribe 2016). Interestingly,
22Additional analysis also attempted to relate the effect of house-
hold debt on banking crises documented earlier to institutional and
country-specific variables, but no significant interaction effects were
detected, probably because of the relatively smaller coverage, over
time, and number of countries and crises observations, relative to the
panel data growth regression analysis.
23In this analysis, capital account openness is measured as de jure
openness. The results do not change when using de facto measures
such as capital flows as a percentage of GDP.
–8
–7
–6
–5
–4
–3
–2
–1
0
0
2
4
6
8
10
12
One year
ahead
Two years
ahead
Three years
ahead
Four years
ahead
Five years
ahead
One year
ahead
Two years
ahead
Three years
ahead
Four years
ahead
Five years
ahead
1. Banking Sector Abnormal Returns
(Regression coefficients)
2. Bank Equity Crash Risk
(Marginal effects)
Source: IMF staff calculations.
Note: Panel 1 shows coefficients from regressions of future bank equity
risk-adjusted abnormal returns, one to five years ahead, using past three-year
changes in the household debt-to-GDP ratio as independent variables. Panel 2
shows the marginal effect of the change in the household debt ratio (normalized
by the standard deviation) on the probability of equity crashes in the next one to
five years. Bank equity crashes are defined as annual bank equity returns lower
than one standard deviation below the mean, as in Cheng, Raina, and Xiong 2014;
and Baron and Xiong 2017. Solid bars mean that the response is statistically
significant using 95 percent confidence intervals.
Figure 2.9. Bank Equity Returns and Household Debt

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this analysis shows that it is the combination of a fixed
exchange rate regime and capital account openness that
magnifies the risks associated with increasing household
debt. This finding is consistent with the limitations that
such a regime poses for accommodating the conse-
quences of large changes in capital inflows (IMF 2016a).
Financial development and the quality of bank
supervision seem to mitigate the medium-term negative
relationship between increases in household debt and
GDP growth. Credit expansion in a more financially
developed environment entails lower risks because the
financial system is better able to assess credit risk and
allocate credit and is better prepared to deal with their
consequences. Moreover, countries where banking
supervision is more stringent and capital requirements
are stricter appear able to reduce the negative effect of
household debt on GDP growth. The same effect is
found for banking systems that have higher capital ratios
or a larger distance to default. All these measures directly
or indirectly reflect the quality and conservatism of the
banking supervision—supervisors may stop banks from
paying out high dividends to shareholders and instead
require them to retain higher capital buffers, thereby
limiting, to some extent, the bank lending channel.
Among institutional variables, the existence of credit
registries significantly reduces the risks signaled by rising
household debt. Having access to broad information on
individuals’ levels of debt and payment histories (both
positive and negative) reduces the possibility of overbor-
rowing, improves origination standards, and reduces
borrowing costs for good creditors. In addition, char-
acteristics of the debt frameworks—such as protection
against predatory lending—temper the negative asso-
ciation with future GDP growth, but are not robustly
significant. Other aspects of the institutional framework,
such as various characteristics of the household credit
market obtained through a survey of country desks, do
not appear to have a significant effect in reducing the
risks signaled by household credit expansion.24
The effect of household debt on GDP is somewhat
larger in more unequal societies. The role of inequal-
ity is not obvious because of two countervailing forces
(Figure 2.10). On one hand, richer households tend
to have lower debt-to-income (DTI) ratios and higher
participation (Figure 2.4). A higher level of inequality
24For the list of housing market characteristics see Annex
Figure 2.1.1. The lack of significance for several of these and other
institutional measures may result from the reduced samples for
which they are available or the limited time variation of the data
(some being available for a single year).
0
10
20
30
40
50
60
(..
.,–
2]
(–
2,
–1
.5
]
(–
1.
5,
–1
]
(–
1,
–0
.5
]
(–
0.
5,
0]
(0
,0
.5
]
(0
.5
,1
]
(1
,1
.5
]
(1
.5
,2
]
[2
,..
.)
Advanced economies Emerging market economies
–1.5
–1.0
–0.5
0
KA
openness Float or fix
Income
inequality Transparency
Financial
dev
Supervisory
strict
1. Distribution of Country-Specific Coefficients
(Relative frequency, percent)
2. Marginal Interaction Effects for Country Factors in High
versus Low Quartiles
(Percent)
Source: IMF staff calculations.
Note: Panel 1 shows country-level coefficients of changes in household debt in
ordinary least squares regressions of three-year-ahead real GDP growth on
changes in household and firm debt. Panel 2 shows the marginal effect of changes
in household debt on GDP growth three years ahead, from panel regressions with
institutional factors, evaluated at the 25th and 75th percentiles. Effects are
statistically significant at the 10 percent level or higher. See also Figure 2.8 and
Annex 2.2. Financial dev = financial development index from Svirydzenka 2016;
Float or fix = exchange rate regime (floating, the green bar, versus fixed, the red
bar); Income inequality = income inequality measures, the difference between the
income share of the top 20 and bottom 20 percent income groups; KA openness =
capital account openness index from the Chinn-Ito Index; Supervisory strict =
measure of overall bank capital stringency from Barth, Caprio, and Levine 2013;
Transparency = dummy variable indicating whether a credit registry or other form
of borrower information data transparency exists.
Figure 2.10. The Impact of Household Debt by Country and
Institutional Factors

70
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International Monetary Fund | October 2017
means that the share of income of the richest households
increases and the macro-level DTI ratio declines.25 On
the other hand, higher-income households may decide
to borrow more as a response to their relatively higher
income, leading to an increase in macro-level DTI. Thus,
the relationship between macro-level household debt and
inequality is ambiguous. In this sample, higher inequality
is associated with a slightly higher impact of changes in
household debt on future growth.26 Other explanations
center on behavior, arguing that higher inequality results
in more people with less financial education who are
more vulnerable to overlending and predatory practices.27
These results suggest that the level of household
debt at which further increases are detrimental is
country specific and higher for countries with better
institutions. The negative effects of increases in the
household debt-to-GDP ratio on future GDP growth
differ by country and depend on the initial level of
indebtedness and country characteristics, as outlined
earlier. This means that countries can attenuate the
negative effects of increased household debt that arise
at high initial levels of indebtedness if they are more
financially developed and have higher standards of
financial information transparency (credit registries)
and consumer finance protection, better regulation and
supervision, less inequality, and more flexible exchange
rate regimes.28 In effect, the impact on growth of a
rising household debt-to-GDP ratio appears to be posi-
tive in the medium term when institutions and policies
are the most effective, and appears to be negative when
institutions and policies are the least effective, regard-
less of the initial level of household debt.
Conclusions and Policy Implications
The econometric analysis clearly shows that house-
hold debt has different effects on economic growth and
financial stability depending on the horizon. At business
cycle frequency, high growth in household lending
appears to foster above-average growth and employ-
25The macro-level DTI is the weighted average of household-level
DTIs, with weights by income share.
26However, the significance of this effect varies, depending on the
exact model specification.
27Along these lines, Rajan (2010) argues that household debt
among lower-income households was encouraged by the political
system in the United States as an easier (but riskier) way to deal with
income inequality.
28While capital openness may also strengthen the association
between household debt and future growth decelerations, it does so
mainly in combination with less flexible exchange rate regimes.
ment at first, but tends to be followed by a period of
instability and subpar GDP growth and employment.
This finding is consistent with the presence of a policy
trade-off between short-term and medium-term growth
and financial instability. While this forecasting trade-off
is a robust pattern of the data, it is stronger for advanced
economies than for emerging market economies, with
increases in household debt consistently signaling higher
risks when initial debt levels are already high. None-
theless, the results indicate that the threshold levels for
household debt increases being associated with negative
macro outcomes start relatively low, at about 30 percent
of GDP. Therefore, although emerging market econo-
mies have some space to take advantage of the positive
effects of expanding households’ access to credit—in
both the short and long term—with low medium-term
risks, such space may be limited. Furthermore, even
in countries with low macro levels of household debt,
a rapid expansion in credit may lead to an increasing
fraction of highly leveraged households that may be
vulnerable to shocks. Finally, existing studies suggest
that household debt appears positive for growth across
medium- to long-term horizons, although the relation-
ship weakens at high levels of indebtedness.
A country’s characteristics, institutions, and policies
can mitigate the risks associated with increasing house-
hold debt. The negative effects are weaker in countries
with less external financing and floating exchange
rates, that are financially more developed, that have
better financial sector regulations and policies, and that
have lower income inequality. Thus, even in countries
where the level of household debt to GDP is high, the
stability-growth trade-off can be attenuated by a com-
bination of good policies, institutions, and regulations.
On the other hand, in countries where the low initial
level of household debt mitigates some of the risks, the
wrong combination of institutional characteristics and
policies may offset the effect of a low debt level. This
indicates that the point at which further increases in
household debt pose risks to future economic perfor-
mance is country specific; various factors should be
evaluated by country authorities to assess vulnerabili-
ties arising from household leverage.
Policy action will need to calibrate the short-,
medium-, and long-term benefits and risks. Policies
need to carefully balance minimizing the medium-term
risks of growth in household credit for financial stabil-
ity without harming the potential long-term benefits
of inclusion and development. Moreover, policy

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action must overcome the inaction bias and political
pressure generated by the very short-term positive
impact of household credit on GDP growth versus the
medium-term negative impact.
In any event, certain policy changes can help
reduce the impact of aggregate demand externalities
and behavioral biases. Some of the drag household
debt places on GDP can be reduced by moving away
from fixed exchange rates; introducing financial sector
policies that promote financial institutions and market
depth, access, and efficiency; and advancing policies
that help reduce income inequality. For the most part,
these policy changes may also have long-term positive
effects on growth. For example, as noted by Coibion
and others (2017), lower inequality may enhance
lower-income households’ access to credit and their
ability to smooth consumption and make long-term
investments (for example, sending children to college
and retraining for different careers) that benefit society.
Furthermore, the reliance on foreign debt and the role
of capital flows may need further attention because
they expose countries to sudden stops or destabilizing
capital outflows (see also IMF 2014).
Macroprudential policy can help curb household
leverage. Macroprudential policies can help internalize
the externality that the borrowing by each household
imposes on the rest of the financial system, given that
large increases in household debt are associated with
a greater likelihood of financial crises and recessions.
The design of targeted macroprudential measures may
need to take distributional aspects into account, since
certain characteristics of households are associated with
a greater misalignment of debt and future income.
Detailed panel regression analysis shows that various
macroprudential measures can significantly reduce real
household credit growth, both in advanced econo-
mies and in emerging market economies (Box 2.5).
Demand-side measures, such as limits on the
debt-service-to-income ratio and loan-to-value ratio,
seem highly effective. Supply-side measures targeted at
loans, such as limits on bank credit growth, loan con-
tract restrictions, and loan loss provisions, are equally
effective. However, these policies would require careful
calibration to maintain the balance between the short-,
medium-, and long-term effects discussed.
There is also a role for policymakers to further
strengthen the protection of consumer finance. The
empirical analysis found that credit registries reduce
the negative effects on growth in the medium term.
The development of credit registries will help improve
the welfare of households vulnerable to overborrowing.
Consumer financial protection not only helps unso-
phisticated consumers make wiser finance decisions, it
also helps enhance overall financial stability, as shown
in the empirical analysis. Measures could include
increasing the transparency of financial contracts,
financial education, prohibition of predatory lending,
and regulation of certain financial innovation products.
Similarly, good microprudential supervision can mit-
igate the negative effects of household debt. As amply
demonstrated during the global financial crisis, differ-
ences in the quality and depth of banking supervision
helped explain why some countries escaped the nega-
tive externalities associated with the large increase in
household debt during the preceding decade. This may
reflect stronger supervisory powers or more stringent
capital regulation frameworks that allowed supervi-
sors to diminish the negative effect of household debt
increases on future GDP.
Market solutions may also help mitigate the eco-
nomic consequences of household debt in financial
recessions. For example, risk sharing between mortgage
lenders and borrowers could be increased, which is
the aim of the shared appreciation design of mortgage
contracts advocated by Shiller (2014) and Mian and
Sufi (2014). In this more equity-like design of mortgage
contracts, the principal is automatically written down
if the local house price index falls below a specified
threshold; increases in property value are shared between
the homeowner and the lender. This type of mortgage
loan can help price in the associated crash risk before
lenders extend credit and reduce the debt overhang
problem of households when house prices fall. In theory,
this approach would reduce the blow to the macroeco-
nomy of housing busts during episodes of household
deleveraging. It would thus enhance financial stability
much as nonfinancial firms or banks benefit from bail-in
debt with loss-absorbing capacity vis-à-vis bondholders
(see Chapter 3 of the October 2013 Global Financial
Stability Report). However, more work is needed on the
conditions and pricing that would entice banks to offer
such contracts and to get a full understanding of the
potential effects on financial stability (including banks’
ability to absorb associated losses).

72
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International Monetary Fund | October 2017
In the long term, higher levels of credit to GDP
are generally associated with higher economic growth.
Financial development, including better institutions
and easier access to credit by households, has been
shown to be beneficial to economic growth in the long
term (Levine 1998; Beck and Levine 2004). As the
financial sector develops, growth-enhancing invest-
ments can be more easily financed. Nonetheless, the
relationship between household debt and growth is
more elusive (Jappelli and Pagano 1994; De Gregorio
1996; Beck and others 2012; Sahay and others 2015a).
Recent studies have found that economies may
reach a point of “too much finance.” Arcand, Berkes,
and Panizza (2015) and Sahay and others (2015b)
found that financial depth begins to dampen out-
put growth when credit to the private sector reaches
between 80 percent and 100 percent of GDP. Too
much finance may increase the frequency of booms
and busts because of greater risk taking and leverage,
and may leave countries ultimately worse off and with
lower real GDP growth. Another argument is that too
much finance leads to a diversion of talent and human
capital away from productive sectors and toward the
financial sector (Shiller 2005).
A more detailed analysis with household credit
suggests the existence of a tipping point. An empirical
exercise conducted for the countries covered in the
chapter finds that household debt increases long-term
real GDP per capita growth, but the effects weaken at
higher levels of household debt and eventually become
negative. The maximum positive impact in this exer-
cise is found when household debt is between 36 per-
cent and 70 percent of GDP (Figure 2.1.1, panel
1). In addition, there does not appear to be an effect
specific to emerging market economies, but a financial
crisis seems to result in permanently lower per capita
GDP growth (Figure 2.1.1, panel 2).
Box prepared by Adrian Alter and Nico Valckx.
–0.5
0
0.5
1.0
1.5
100 20 30 40 50 60 70 80 90 100 110
Lo
ng
-t
er
m
p
er
c
ap
ita
G
D
P
gr
ow
th
Household debt-to-GDP ratio
95 percent
confidence
bound
around the
turning point
(1)
Variables Per Capita
GDP Growth
(2)
Per Capita
GDP Growth
(3)
Per Capita
GDP Growth
HHD
HHD2
Crisis
EME × HHD
Education
Constant
Observations
Number of
countries
AR2
Hansen
Instruments
Initial per
capita GDP
Source: IMF staff calculations.
Note: Figure shows nonlinear effect of household debt on
long-term per capita GDP growth at various levels of
household debt, based on a long-term panel regression. It
uses the Arellano-Bover general method of moments
estimator of five-year average per capita GDP growth
(shown in panel 2) on household debt to GDP (HHD), the
squared ratio of household debt to GDP (HHD2), initial per
capita GDP, secondary education enrollment, dummies for
banking crises (Crisis), and emerging market economies’
household debt-to-GDP ratio (EME × HHD).
*** p < 0.01; ** p < 0.05; * p < 0.1. 1Z-statistics in parentheses. Figure 2.1.1. Long-Term per Capita GDP Growth and Household Debt 1. Effect of Household Debt on per Capita GDP Growth (Percent) 2. Panel Regression of per Capita GDP Growth and Household Debt, 1970–20101 0.051* 0.007 0.021 (1.726) (0.346) (0.762) –0.048** –0.024 –0.051** (–1.980) (–1.494) (–2.057) –0.017*** –0.015*** (–6.319) (–4.688) –0.000 (–0.015) 0.028 0.018* 0.017 (1.117) (1.818) (1.576) –0.012** –0.004 –0.000 (–1.973) (–1.227) (–0.078) –0.035 –0.038 –0.066 (–0.353) (–0.933) (–1.507) 278 278 278 73 73 73 0.0186 0.137 0.185 0.253 0.797 0.361 55 73 68 Box 2.1. Long-Term Growth and Household Debt 73 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 Housing assets and mortgages are important com- ponents of the balance sheets of Chinese households. High levels of ownership (about 90 percent of the population own a property) make housing the largest asset of Chinese households: more than two-thirds of their total assets (Figure 2.2.1, panels 1 and 2). On the liability side, urban households in China have increased their borrowing. Mortgage loans from banks account for the largest share of their debt. Consistent with the life-cycle theory of debt, participation rates among urban Chinese households across age groups follow a hump shape and are highest for younger 0 25 50 75 100 Q1 Q2 Q3 Q4 Q5 Housing-to-assets ratio Homeownership Mortgage-to-debt ratio 0 10 20 30 40 Q 1 Q 2 Q 3 Q 4 Q 5 15 –2 9 30 –4 4 45 –5 9 60 + All Income quintile Income quintile Age (years) Q1 Q2 Q3 Q4 Q5 Q1 Q2Q3 Q4 Q5 0 20 40 60 80 100 0 200 400 600 800 D eb t- se rv ic e- to -i nc om e ra tio Debt-to-income ratio 2011 2015 0 10 20 30 40 Q1 Q2 Q3 Q4 Q5 Income quintile 2011 2015 13 14 15 16 17 18 Debt-to-income ratio = 1 Debt-to-income ratio = 5 Below 2 Between 2 and 3 Between 3 and 4 Above 4 Sources: IMF staff calculations, based on China Household Finance Survey; see Gan and others 2013 for details. Note: Data shown are mainly for urban households from different income quintiles (Q1 to Q5, lowest to highest). The housing-to-assets ratio is defined as the ratio of housing assets to total assets. The mortgage-to-debt ratio is defined as the ratio of mortgage debt to total debt. The mortgage debt participation rate is computed across age groups. Debt-to-income (multiple) and debt-service-to-income (percentage) ratios by income quintiles are scaled by the share of each household quintile in total debt. The response of consumption-to-income shocks is the coefficient in the cross-sectional regressions of the percentage change in consumption on the percentage change in income between 2013 and 2015 among households that were tracked in the survey. In panel 2, “age” refers to the age of the head of household. For panel 5, a ratio above 4 indicates a highly indebted household. Figure 2.2.1. Characteristics of China’s Household Debt (Percent) 1. Housing-to-Assets and Mortgage-to-Debt Ratios, and Homeownership 2. Mortgage Participation Rate 3. Debt-to-Income and Debt-Service-to- Income Ratio 4. Loan Balance-to-Value Ratio 5. Distribution of Household Debt by Debt-to-Income Groups 6. Response of Consumption to Income Shocks Box 2.2. Distributional Aspects of Household Debt in China 74 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 households.1 Household debt has become an increas- ingly important component of credit in China. As the household debt-to-GDP ratio rose from 18.7 per- cent to about 38 percent from 2007 to 2016, loans to households as a percentage of total loans issued by financial institutions increased from 19.4 percent to 31.3 percent over the same period.2 The debt burden of mortgage borrowers in urban areas has increased in recent years, although mortgage participation rates are still relatively low compared with advanced economies. The debt-to-income ratio increased across most income groups, especially for lower-income households. The debt service ratio, defined as total debt repayment as a percentage of total income, also increased for all income groups but espe- cially for lower-income households (Figure 2.2.1, panel 3). The loan balance-to-value ratio, defined as the remaining loan balance as a percentage of self-reported housing value, also increased over time (Figure 2.2.1, panel 4). On the other hand, mortgage loan partici- pation rates, especially for low-income households, are 1Note that not many households of those ages 45–59 borrow for mortgages because a large share of today’s housing stock still originates from the planned-economy period during which the government or state-owned enterprises distributed housing. 2Only domestic-currency (renminbi) loans are included. Data on total loans and loans to households are based on Sources and Uses of Funds of Financial Institutions published by the People’s Bank of China. still low, which is consistent with China’s economic and financial development level. The increased household debt could amplify the macroeconomic consequences of negative shocks. Although household debt is about 38 percent of GDP in China, more than one-third of it is held by highly indebted households, defined as those with a debt-to-income ratio greater than 4 (Figure 2.2.1, panel 5). This means that deterioration in the balance sheets of these households could have an amplified negative impact on the banking sector as well as on the macroeconomy, even though loans to house- holds, including home mortgages, in China are still a smaller fraction of banks’ total assets than in advanced economies. In addition, empirical evidence based on tracked samples of Chinese households between 2013 and 2015 shows that consumption of households with high debt to income responds more strongly to income shocks (Figure 2.2.1, panel 6). This suggests that negative shocks to household balance sheets may amplify the effect on China’s economy because of highly indebted households’ higher marginal propen- sity to consume—a pattern consistent with evidence in advanced economies (for example, Mian, Rao, and Sufi 2013). Box prepared by Alan Xiaochen Feng, in collaboration with Feng Li and Xiaomeng Lu from the Survey and Research Center for China Household Finance at Southwestern University of Finance and Economics. Box 2.2 (continued) 75 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 Until the global financial crisis, household debt levels evolved very similarly in the United States and Canada. US household debt increased from 56 percent in 1995 to nearly 100 percent of GDP in the first quarter of 2008 and from 62 percent to 80 percent in Canada (Figure 2.3.1, panel 1). Afterward, US household debt fell to below 80 percent by early 2017, whereas in Canada, it continued to rise to more than 100 percent. This reflects different house price and unemployment trends, as well as difference in the evo- lution of net wealth, which left Canadian households relatively better off than their US counterparts. Box prepared by Adrian Alter, Alan Xiaochen Feng, and Nico Valckx. The composition of household debt has changed in both countries. In response to continuously rising house prices, Canadian household debt became more tilted toward mortgage debt, which increased from 61 percent of total debt in 2005 to 66 percent of total debt in 2016 (Figure 2.3.1, panel 2). In the United States, where house prices fell by 40 percent from their peak in 2008, households’ share of mortgage debt decreased, while consumer debt increased substantially, mainly because of increased student loan debt. Leverage is very different across households. US households’ leverage (as given by the debt-to-income ratio) remained broadly constant, except for the poorest income group, whose leverage increased slightly. In Canada, on the other hand, debt-to-income 80 120 160 200 240 40 70 100 130 1995 99 2003 07 11 15 In de x Pe rc en t Canadian debt (left scale) US debt (left scale) Canadian real house price (right scale) US real house price (right scale) 50 60 70 80 90 100 2005 16 2005 16 Canada United States Mortgage Consumer Other 0 200 400 600 Q5 Income quintile 2004 2013 0 200 400 600 Q1 Q2 Q3 Q4Q1 Q2 Q3 Q4 Q5 Income quintile 2005 2012 Source: IMF staff calculations, based on the Luxembourg Wealth Study, US Survey of Consumer Finances, and the Canadian Survey of Financial Security. Note: Panels 3 and 4 refer to the median debt-to-income levels by income quintiles for mortgage borrowers. Figure 2.3.1. US and Canadian Household Debt Developments and Characteristics 1. Household Debt-to-GDP Ratio and House Prices 2. Composition of Household Debt (Percent) 3. United States: Debt-to-Income Ratio Distribution (Percent) 4. Canada: Debt-to-Income Ratio Distribution (Percent) Box 2.3. A Comparison of US and Canadian Household Debt 76 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 ratios increased across all income groups, resulting in an average ratio almost 50 percent higher than in the United States (Figure 2.3.1, panels 3 and 4). Moreover, highly indebted households (those with debt-to-income ratios above 350 percent) held more than Can$400 billion, or 21 percent of the total household debt in Canada at the end of 2014, up from 13 percent before the crisis (Bank of Canada 2015). High leverage may expose households to poten- tially adverse income shocks. The past recession in the United States showed that highly indebted households substantially reduced spending, which contributed to a significant decline in aggregate demand (Mian and Sufi 2011). Results reported in this chapter are in line with analysis by the Bank of Canada, which in its latest Financial System Review highlighted high household indebtedness and imbalances in the Canadian housing market as its two most important vulnerabilities; accordingly, it has implemented several macroprudential measures to mitigate these prob- lems (IMF 2017). Box 2.3 (continued) 77 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 Household debt leads to higher house prices and more debt in the future, likely through reinforcing feedback effects. Dynamic panel vector autoregression analysis confirms that household debt has a short-term positive effect on real house prices and output.1 A one standard deviation shock to household debt initially leads to higher real house prices and output, but over the medium term (after about three to five years) results Box prepared by Adrian Alter and Alan Xiaochen Feng. 1The panel vector autoregression model was conducted with a set of 27 countries with quarterly data available starting in 1998. in a decline (Figure 2.4.1, panels 1 and 3).2 Higher house prices are positively associated with output in the short and medium term, but negatively in the long term (Figure 2.4.2). In response to a positive shock to house prices, household debt increases steadily over the short and medium term, while reverting to its long-term mean thereafter (Figure 2.4.1, panel 4). 2These findings are consistent with Lombardi, Mohanty, and Shim 2017. See also Mian, Sufi, and Verner, forthcoming; Calza, Monacelli, and Stracca 2013; and Brunnermeier and others 2017. –0.8 –0.4 0.0 0.4 0.8 0 4 8 12 16 20 24 Quarter –0.5 0.0 0.5 0 4 8 12 16 20 24 Quarter –2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 0 4 8 12 16 20 24 Quarter –0.3 0.0 0.3 0.5 0.8 1.0 0 4 8 12 16 20 24 Quarter Source: IMF staff calculations. Note: The figure presents impulse responses from a five-variable recursive panel vector autoregression with eight lags using quarterly data from 1998:Q1 to 2015:Q4, which includes country and time fixed effects. Shocks are identified using a Cholesky decomposition with the following order: log real GDP, corporate debt, household debt, log real house prices, and short-term interest rates. Household debt and corporate debt were scaled by GDP. The results are robust to a Nickell bias correction (using panel general method of moments techniques) and other specifications (for example, ordering, number of lags, changes instead of levels). Dashed lines represent 90 percent confidence intervals, computed using 500 Monte Carlo simulations. Figure 2.4.1. Panel Vector Autoregression Dynamic Analysis (Percentage points) 1. Shocks to Household Debt Ratio: Effect on Real Output 2. Shocks to House Prices: Effect on Real Output 3. Shocks to Household Debt Ratio: Effect on House Prices 4. Shocks to House Prices: Effect on Household Debt Ratio Box 2.4. The Nexus between Household Debt, House Prices, and Output 78 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Micro-level panel survey data analysis confirms the impact of house prices on consumption and the role of debt. In Korea, the rise in the local house price index between 2008 and 2014 had a positive effect on household consumption, which is consistent with the initial positive response of GDP to house price shocks shown in the panel vector autoregression analysis.3 3This empirical exercise uses tracked samples of households between 2008 and 2014 and controls for changes in household income, demographic information, and city-level aggregates. Such an effect is present only for homeowners, suggesting that the increase in house prices raises collateral value as well as perceived wealth for these households (Figure 2.4.2, panel 1). Similarly, in Australia, homeowners increased consumption in response to higher local house prices between 2012 and 2015, and the effect was stronger for house- holds with high financial leverage. This finding indicates that higher household debt reinforces the impact of house prices on the real economy (Figure 2.4.2, panel 2). –0.1 0 0.1 0.2 0.3 0.4 Homeowner Non–homeowner 0 0.1 0.2 0.3 Debt-to-income ratio = 2 Debt-to-income ratio = 4 Sources: Australian Bureau of Statistics; Household, Income and Labour Dynamics in Australia; Korean Labor and Income Panel Study; Statistics Korea; and IMF staff calculations. Note: For households in Korea, regression coefficients are obtained by regressing the percentage change in consumption on changes in the local house price index between 2008 and 2014. For households in Australia, regression coefficients are obtained by regressing the percentage change in consumption on changes in the local house price index between 2012 and 2015. In both analyses, controls include the percentage change in household income, debt, and other demographic information, as well as state-level changes in income over the same period. Samples of households in both countries are restricted to those tracked over the period covered. Low leverage corresponds to a debt-to-income ratio of 2 and high leverage corresponds to a debt-to-income ratio of 4. Standard errors are clustered at the state or province level. Figure 2.4.2. Consumption Response to House Prices (Percent) 1. Household Consumption in Korea 2. Household Consumption in Australia Box 2.4 (continued) 79 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 This box finds that macroprudential loan-targeted measures successfully reduce the growth of real household credit in both advanced economies and emerging market economies. Many countries introduced or tightened macropru- dential policy measures to limit systemic risk in the aftermath of the large credit boom that preceded the Box prepared by Adrian Alter and Machiko Narita. global financial crisis (Figure 2.5.1, panel 1). In theory, macroprudential policies reduce systemic risk by correct- ing externalities operating through the financial system. Such externalities include aggregate demand externalities and strategic complementarities among financial institu- tions, which amplify credit and asset price cycles.1 1See, for example, Hanson, Kashyap, and Stein 2011; De Nicolò, Favara, and Ratnovski 2012; and IMF 2013. –30 –20 –10 0 10 20 30 –80 –60 –40 –20 0 20 40 60 80 1990 92 94 96 98 2000 02 04 06 08 10 12 14 16 EME macroprudential policies (cumulative sum, left scale) AE macroprudential policies (cumulative sum, left scale) EME real household credit (year-over-year growth, percent, right scale) AE real household credit (year-over-year growth, percent, right scale) –3 –2 –1 0 1 D eb t- se rv ic e- to - in co m e ra tio Lo an -t o- va lu e ra tio Lo an o r bo rr ow in g lim its o r pr oh ib iti on s Li m its o n ba nk cr ed it gr ow th Lo an lo ss pr ov is io ns Re se rv e re qu ire m en ts Le ve ra ge r at io Co un te rc yc lic al ca pi ta l b uf fe r Li m its o n fo re ig n ex ch an ge p os iti on s All AEs EMEs All AEs EMEs –1.5 –1.2 –0.9 –0.6 –0.3 0.0 0.3 Al l Lo an D em an d Su pp ly G en er al Ca pi ta l Lo an s Supply Macroprudential policies Source: IMF staff calculations. Note: In panel 1, the macroprudential policies show the cumulative sum of tightening (+) and loosening (–) policies. Panel 2 shows the estimated average effects on real household credit growth of one tightening event for each macroprudential measure, one at a time, in a panel regression of 62 countries (32 advanced economies and 30 emerging market economies). In panel 3, All comprises all 14 measures considered. Loan consists of demand-side and supply-side loans. Demand includes debt-service-to-income ratios and loan-to-value ratios. Supply measures are classified into General, Capital, and Loans. Supply (General) consists of reserve requirements, liquidity requirements, limits on foreign exchange positions, and taxes on financial institutions. Supply (Capital) consists of capital requirements, conservation buffers, the leverage ratio, and the countercyclical capital buffer. Supply (Loans) consists of limits on bank credit growth, loan loss provisions, loan restrictions, and limits on foreign currency loans. Shaded bars depict significant effects at the 10 percent confidence levels. See Annex 2.2 for estimation details. AEs = advanced economies; EMEs = emerging market economies. Figure 2.5.1. Macroprudential Policy Tools and Household Credit Growth 1. Number of Macroprudential Policies and Real Household Credit Growth 2. Effect of Individual Macroprudential Tools (Percentage points) 3. Effect of Combined Policies, Average by Type (Percentage points) Box 2.5. The Impact of Macroprudential Policies on Household Credit 80 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 In both advanced and emerging market econo- mies, targeted macroprudential measures successfully reduce real household credit growth. From a set of 14 measures, 5 measures related to credit have robust negative effects (Figure 2.5.1, panel 2). These measures are limits on the debt-service-to-income (DSTI) ratio, limits on the loan-to-value (LTV) ratio, loan contract restrictions, limits on bank credit growth, and loan loss provisions. On average, a tightening of these measures leads to a 1 to 3 percentage point decline in real household credit growth, similar to Kuttner and Shim’s (2016) results for LTV and DSTI ratio limits.2 The effects are generally stronger in emerging market economies, corroborating the findings of Cerutti and others (2017).3 On the other hand, measures that are not targeted to loans do not exhibit strong effects in contracting household credit. Reserve requirements also tend to 2Other studies, using different data and methodologies, also show that tighter LTV and DSTI ratios reduce household credit growth. See Lim and others 2011; Arregui and others 2013; Crowe and others 2013; Krznar and Morsink 2014; and Jácome and Mitra 2015. 3Loan restrictions and limits on credit growth also appear to effectively contain corporate credit growth, to the tune of 2 to 3 percentage points, while other measures have a weak or insignificant impact. The latter could reflect firms’ better access to (international) debt markets than households. have negative effects, but they are smaller and less significant than targeted measures.4 Leverage limits, conservation buffers, and limits on foreign exchange positions are positively associated with subsequent growth in household credit. Other measures, such as capital requirements and taxes on financial interme- diaries, do not have significant effects. However, a tightening of general supply measures should increase the resilience of the financial system to aggregate shocks by building buffers. Previous studies also find weaker effects of nontargeted and capital measures and may explain their lack of effectiveness, including leakages. For example, tightening capital require- ments may have little effect when banks hold ample capital. When examining the effects of measures by type, demand-side measures (DSTI and LTV) as well as loan-targeted supply-side measures (on domestic credit growth and loan loss provisions) are found to be effective (Figure 2.5.1, panel 3).5 4See Arregui and others 2013; Crowe and others 2013; Vandenbussche, Vogel, and Detragiache 2015; and Kuttner and Shim 2016. 5Combining same-type measures allows the effects of multiple measures adjusted at the same time to be controlled for. For example, Kuttner and Shim (2016) report that changes in DSTI and LTV ratio limits are often coordinated. Box 2.5 (continued) 81 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 Annex 2.1. Data Sources Annex Table 2.1.1. Countries Included in the Sample for Household Debt and Data Sources Country Source Start Year Country Source Start Year Advanced Economies Emerging Market Economies Australia BIS; JST 1952 Argentina BIS 1994 Austria BIS 1995 Bangladesh Haver 2004 Belgium BIS; JST 1950 Bolivia Central Bank of Bolivia 1992 Canada BIS; JST 1956 Botswana IMF, MFS 2001 Cyprus CEIC 1995 Brazil BIS 1994 Czech Republic BIS 1995 Bulgaria ECRI 1995 Denmark BIS; JST 1951 Chile BIS; Central Bank of Chile 1983 Estonia Haver; Bank of Estonia 1993 China BIS 2006 Finland BIS; JST 1950 Colombia BIS 1996 France BIS; JST 1958 Costa Rica Central Bank of Costa Rica 1997 Germany BIS; JST 1950 Croatia Croatian National Bank 1993 Greece Haver 1980 Egypt Central Bank of Egypt 2002 Hong Kong SAR CEIC 1982 FYR Macedonia National Bank of the Republic of Macedonia 1995 Iceland Haver; IMF, MFS 1995 Georgia IMF, MFS 2001 Ireland ECRI 1998 Ghana IMF Bridge Data; IMF, MFS 2001 Israel BIS 1992 Hungary BIS 1989 Italy BIS 1950 India CEIC 1998 Japan BIS; JST 1950 Indonesia BIS 2001 Korea BIS 1962 Jordan Central Bank of Jordan 1993 Latvia Haver 2003 Kazakhstan Haver 1996 Lithuania Haver 1993 Kenya IMF, MFS 2001 Luxembourg Haver 1992 Kuwait CEIC 1997 Malta ECRI 1995 Malaysia IMF, MFS 2001 Netherlands BIS 1990 Mauritius IMF, MFS 2001 New Zealand BIS 1990 Mexico BIS 1994 Norway BIS 1975 Mongolia IMF, MFS 2001 Portugal BIS 1979 Montenegro ECRI 1995 Singapore BIS 1991 Morocco IMF, MFS 2001 Slovak Republic National Bank of Slovakia 1993 Namibia IMF, MFS 2001 Slovenia Haver; IMF, MFS 2004 Nigeria IMF, MFS 2001 Spain BIS; JST 1950 Pakistan IMF, MFS 2006 Sweden BIS; JST 1975 Panama IMF, MFS 2002 Switzerland BIS; JST 1950 Paraguay Central Bank of Paraguay; IMF, MFS 1990 United Kingdom BIS; JST 1950 Philippines Central Bank of the Philippines 1999 United States BIS; JST; CEIC 1950 Poland BIS 1995 Romania ECRI 1996 Russia BIS 1995 Saudi Arabia BIS; CEIC 1995 Serbia IMF, MFS 2003 South Africa Haver 1969 Thailand BIS 1991 Turkey BIS 1986 Ukraine IMF, MFS 2001 Uruguay BIS 2001 Venezuela BIS 2001 Sources: IMF staff. Note: BIS = Bank for International Settlements; CEIC = CEIC Data Co. Ltd.; ECRI = Economic Cycle Research Institute; Haver = Haver Analytics; IMF, MFS = Monetary and Financial Statistics database; JST = Jordà-Schularick-Taylor Macrohistory Database. 82 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 0 20 40 60 80 100 REC FIX PEN GOV NAT TAXD TAXL TRA PROT Advanced economies Emerging market economies Source: IMF staff calculations. Note: Figure is based on an IMF desk survey of the prevalence of certain debt characteristics in 80 countries. The desk survey reveals that a majority of countries have financial protection regulations (against predatory lending practices) and loan transparency rules and regulations (through credit registries or credit bureaus). In 80 percent of the sample, recourse is commonplace in loan agreements, whereas early prepayment restrictions feature in about 40 percent of the countries surveyed. Tax deductibility is common in half of the sample, with limitations on how much debt (or interest payments) households can deduct from their taxes. Fixed-rate mortgages (with the initial rate fixed for 10 or more years) are offered in most countries. Administrative restrictions on land supply are more prevalent in advanced economies (about 60 percent) than in emerging market economies (44 percent), whereas natural restrictions exist in about 30 percent of the countries surveyed (related to size of the country, livable land area, population density, and the like). FIX = fixed rates are offered; GOV = administrative restrictions on land supply; NAT= natural restrictions on density of development, such as topography and geography; PEN = restrictions on early payment; PROT = consumer financial protection legislation in place; REC = mortgage loans are full recourse; TAXD = debt or interest payments are tax deductible; TAXL = limits on TAXD exist; TRA = credit registry. Annex Figure 2.1.1. Loan Characteristics, Rules, and Regulations Annex Table 2.1.2. Household Survey Data Sources Country Name of Survey Advanced Economies Australia Household, Income and Labour Dynamics in Australia Survey Canada Luxembourg Wealth Study, Survey of Financial Security Euro Area European Central Bank’s Household Finance and Consumption Survey; Luxembourg Income Study (LIS); Luxembourg Wealth Study (LWS) Japan Keio Household Panel Survey Korea Korean Labor and Income Panel Study; Korean Statistical Information Service Netherlands DNB Household Survey United Kingdom British Household Panel Survey United States Luxembourg Wealth Study, Survey of Consumer Finances Emerging Market Economies China China Household Finance Survey Source: IMF staff. 83 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 Annex Table 2.1.3. Description of Explanatory Variables Used in the Chapter Variables Description Source Macro-level Variables Nominal GDP Gross domestic product, current prices, national currency Jordà-Schularick-Taylor Macrohistory database; Penn World Table; IMF, World Economic Outlook database Real GDP Gross domestic product, constant prices, national currency IMF, World Economic Outlook database Real Private Consumption Private final consumption, constant prices, national currency IMF, World Economic Outlook database Consumer Price Index Consumer prices, period average, index IMF, International Financial Statistics database Population Population, in millions of persons IMF, World Economic Outlook database Unemployment Unemployment rate (percent) IMF, World Economic Outlook database Interest Rate Three-month Treasury bill rate, money market rate, interbank market rate (percent) Bloomberg Finance L.P.; IMF, International Financial Statistics database; Thomson Reuters Datastream Bank Equity Index Equity price index of the banking sector (or financial sector if banking sector price index not available) Bloomberg Finance L.P.; Thomson Reuters Datastream Stock Market Index Overall stock price index Bloomberg Finance L.P.; IMF, Global Data Source database; Thomson Reuters Datastream Banking Crisis Systemic banking crisis defined as (1) significant signs of financial distress in the banking system (as indicated by significant bank runs, losses in the banking system, and/or bank liquidations); (2) significant banking policy intervention measures in response to significant losses in the banking system Laeven and Valencia 2013 Real House Price Index House price index deflated by consumer price index Jordà-Schularick-Taylor Macrohistory database; OECD, Global Property Guide; and IMF staff calculations Exchange Rate National currency units per US dollar, period average Thomson Reuters Datastream Real Effective Exchange Rate Real effective exchange rate, based on consumer price index IMF, Monetary and Financial Statistics database Exchange Rate Regime De facto exchange rate arrangement of the country Ilzetzki, Reinhart, and Rogoff 2017 data set Institutional Variables Financial Risk Index Measure of a country’s ability to pay its way by financing its official, commercial, and trade debt obligations; index ranges from 50 (least risk) to a low of 0 (highest risk) International Country Risk Guide, PRS Group Financial Development Index Overall financial development index Svirydzenka 2016 Capital Account Openness Index (Chinn-Ito Index) An index measuring a country’s degree of capital account openness Chinn and Ito 2006 data set (updated) Official Supervisory Power Whether the supervisory authorities have the authority to take specific actions to prevent and correct problems; index ranges from 0 (no powers) to 14 (most powers) Barth, Caprio, and Levine 2013 Overall Capital Stringency Whether the capital requirement reflects certain risk elements and deducts certain market value losses from capital before minimum capital adequacy is determined; index ranges from 0 (least stringent) to 7 (most stringent) Barth, Caprio, and Levine 2013 Income Share Held by Highest 20 Percent Percentage share of income or consumption is the share that accrues to subgroups of the population indicated by deciles or quintiles World Bank, World Development Indicators Income Share Held by Lowest 20 Percent Percentage share of income or consumption is the share that accrues to subgroups of the population indicated by deciles or quintiles World Bank, World Development Indicators Source: IMF staff. Note: OECD = Organisation for Economic Co-operation and Development. 84 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Annex 2.2. Methodology This annex provides a general overview of the meth- odologies behind the various econometric exercises performed in this chapter. Logit Analysis The logit model analyzes how levels and changes in household debt affect financial stability. The model is given by log P [ S it = 1 | X it ] __________ P [ S it = 0 | X it ] = Ψ 0i + Ψ 1 X it + Ψ 2 X it I (HiDebt) it + ϵ it , (A2.2.1) in which Xit refers to a vector of lagged changes and levels of household and corporate debt-to-GDP ratios, while the third term refers to interactions with an indicator I (HiDebt). The latter takes the value of one if country i experiences household debt exceed- ing 65 percent of GDP. Country fixed effects ( Ψ 0i ) were included in the estimation. The main metric to compare model performance is the area under curve. Annex Table 2.2.1 contains the underlying estimates. Household Debt and Bank Equity Returns This exercise provides an alternative measure of banking stress and assesses the role of household debt for future bank equity returns. According to the effi- cient market hypothesis, past household credit growth should not be correlated with future bank stock returns if investors correctly price the risks associated with the rise in household debt to the banking sector. However, downside risks may be neglected by investors during credit booms when market sentiments are high (for example, Cheng, Raina, and Xiong 2014; Baron and Xiong 2017), leading to systematic predictability of bank stock declines following increases in household debt. Following Baron and Xiong (2017), the empiri- cal specification is given by r c,t + k − r c,t + k f = α c + γ t + β h Δ ( HHD _____ GDP ) c,t + β f Δ ( NFCD _____ GDP ) c,t + β d × DivYl d c,t + X c,t δ + ϵ c,t , (A2.2.2) in which r c,t+k is the return in year k of the bank- ing sector index in country c; is government bond Annex Table 2.2.1. Logit Analysis: Probability of Systemic Banking Crisis Variables (1) (2) (3) (4) (5) Dependent Variable: Systemic Banking Crises Household Debt 4.037*** 2.501*** 1.270 2.091 (0.783) (0.925) (1.276) (1.716) Δ Household Debt 40.05*** 35.01*** 35.60*** 30.86*** (6.482) (6.334) (7.161) (8.451) Corporate Debt 0.879 0.536 (0.761) (0.743) Δ Corporate Debt 13.13*** 15.62*** (3.954) (4.220) Δ Household Debt × High HH Debt 24.41* (14.11) High HH Debt −1.355 (0.896) Constant −5.949*** −3.741*** −5.465*** −5.224*** −5.253*** (0.594) (0.150) (0.681) (0.732) (0.902) Observations 1,223 1,033 1,033 1,020 1,020 Country Cluster Yes Yes Yes Yes Yes Country Fixed Effect Yes Yes Yes Yes Yes Area under Curve 0.700 0.791 0.806 0.840 0.850 Number of Crises 46 37 37 37 37 Number of Clusters 40 34 34 34 34 Pseudo R 2 0.0612 0.142 0.153 0.204 0.218 Source: IMF staff calculations. Note: Robust standard errors in parentheses. All regressors are lagged. The third lag of household debt change was used based on significance. High household debt (High HH Debt) dummy variable is set at 65 percent of GDP, representing the top quintile of the distribution. Banking crises are taken from the updated database by Laeven and Valencia (2013). *** p < 0.01; ** p < 0.05; * p < 0.1. 85 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 yield, and DivYl d c,t is the dividend yield of the banking sector, Δ ( HHD _____ GDP ) c,t = ( HHD _____ GDP ) c,t − ( HHD _____ GDP ) c,t − 1 and Δ ( NFCD _____ GDP ) c,t = ( NFCD _____ GDP ) c,t − ( NFCD _____ GDP ) c,t − 1 (A2.2.3) normalized by the standard deviation of each variable for each country, and X c,t includes control variables such as the past levels of household debt and corporate debt ratios. The baseline model is estimated using the specifi- cation above. Two similar models are also estimated using probit analysis and quantile regressions. The probit analysis examines the relationship between past increases in the household debt ratio and the probabil- ity of bank equity crashes occurring in the next one to five years. Bank equity crashes are defined as having an annual stock return below the mean return by at least one standard deviation. In the quantile regressions, the relationship between past increases in the household debt ratio and future bank equity returns at different quantiles is examined. Time Series Analysis of Household Debt, Income, and Consumption Panel regressions are estimated following Mian, Sufi, and Verner, forthcoming, estimating future real GDP growth on changes in household debt and corporate debt ratios and lagged GDP growth rates. Different specifications are estimated, with changes in the debt ratio calculated over the past three years. In addition, level effects, thresholds, and nonlinearities are tested. Regression estimates are further differentiated by var- ious groupings: advanced and emerging market econ- omies, various institutional factors, and loan terms. Estimations are also performed over different time periods (before and after the global financial crisis) and were qualitatively very similar. Specifically, the following general equation was estimated: Δ h y i,t + h = α i h + β HH h Δ 3 d i,t − 1 HH + β F h Δ 3 d i,t − 1 F + X i,t − 1 Γ h + ϵ it h (A2.2.4) in which α i h are country fixed effects, Δ3 refers to three-year differences, d i,t HH and d i,t F are the household debt-to-GDP ratio and nonfinancial firm debt-to-GDP ratio, and h = 0, . . . ,6 is the forecast horizon. The matrix Xit includes higher-order lags of the dependent variable as additional controls. Right-hand variables are lagged by one year. Annex Table 2.2.2. provides a summary of the major panel regression estimates. Micro Data Analysis Euro area panel data allow the effects of household leverage on consumption, using a longitudinal house- hold panel, to be tested. Specifically, from a broader euro area household finance and consumption survey of 15 to 20 countries for 2010 and 2014, data for Belgium, Cyprus, Germany, Malta, and the Nether- lands allow testing for the effects of initial household debt-to-income and loan-to-value ratios on changes in the consumption-to-income ratio. The following cross-sectional regression is estimated, at the household level, with change in household food consumption (percent of income) as the depen- dent variable: Δ C i,2014 = α c + β 1 DT I i,2010 + γControls + ϵ i , (A2.2.5) in which debt-to-income ratio (DTIi,2010) is a proxy for past household indebtedness; household charac- teristics (such as employment, education, age of the household head, household’s net wealth and size) are considered Controls. In addition, the model includes country fixed effects ( α c ). Macroprudential Policies and Household Credit Growth Analysis in Box 2.5 gauged the effectiveness of macroprudential tools for reducing household credit growth. More specifically, the following panel regres- sion equation was estimated: C i,t = ρ C i,t − 1 + β MaPP i,t − 1 + γ X i,t − 1 + α i + ​μ​ t + ϵ i,t , (A2.2.6) in which α i and ​​μ​ t denote country and year fixed effects, i denotes country, and t the time period (quarter). The dependent variable, C i,t , refers to year-over-year growth rate of real household credit. The main independent variable, MaPP, is the policy change indicator (that is, tightening or loosening) compiled by IMF staff for each of the 14 macroprudential tools (that is, limits on the debt-service-to-income ratio, loan-to-value ratio, loan restrictions, limits on bank 86 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 An ne x Ta bl e 2. 2. 2. P an el R eg re ss io n Es tim at es f or T hr ee -Y ea r- Ah ea d G ro w th R eg re ss io n on H ou se ho ld D eb t an d Po lic y In te ra ct io n Va ri ab le s (1 ) (2 ) (3 ) (4 ) (5 ) (6 ) (7 ) (8 ) (9 ) Ch an ge H H D /G D P 0. 10 4 0. 14 1* ** −0 .5 88 * −0 .0 62 −0 .2 41 * −0 .1 09 0. 01 5 −0 .7 58 ** −0 .7 96 * Ch an ge F irm D /G D P −0 .0 36 * −0 .0 37 * −0 .0 28 * −0 .0 34 * −0 .0 37 * −0 .0 35 * −0 .0 12 * −0 .0 32 * −0 .0 31 * H H D 30 × Δ H H D −0 .2 61 * −0 .3 67 * −0 .3 73 * −0 .3 60 * −0 .2 80 * −0 .4 35 * −0 .0 80 * −0 .3 10 * −0 .3 04 * Fi na nc ia l O pe nn es s In de x × Δ H H D −0 .1 20 * −0 .1 23 * −0 .0 93 * Fi xe d FX × Δ H H D −0 .3 01 * −0 .1 13 ** * 0. 03 2 Fi na nc ia l R is k In de x × Δ H H D 0. 01 6* 0. 02 0* 0. 01 9* In co m e In eq ua lit y × Δ H H D 0. 00 2 −0 .0 06 ** * −0 .0 04 * Tr an sp ar en cy × Δ H H D 0. 28 5* 0. 24 6* 0. 20 2* Fi na nc ia l D ev el op m en t I nd ex × Δ H H D 0. 36 9* 0. 39 4* ** 0. 44 5* * Fi na nc ia l O pe nn es s In de x 0. 03 −0 .5 88 × Fi xe d FX Fi na nc ia l O pe nn es s In de x −0 .0 58 * −0 .0 90 ** × Fi xe d FX × Δ H H D R 2 Ad ju st ed 0. 58 1 0. 57 2 0. 57 5 0. 56 0. 57 0. 56 8 0. 58 5 0. 61 6 0. 61 8 O bs er va tio ns 1, 00 2 1, 00 2 1, 00 2 1, 00 2 1, 00 2 1, 00 2 1, 00 2 1, 00 2 1, 00 2 N um be r of C ou nt rie s 57 57 57 57 57 57 57 57 57 Ak ai ke In fo rm at io n Cr ite rio n 6. 16 6. 18 6. 17 6. 2 6. 18 6. 19 3. 95 6. 08 6. 07 F- st at is tic 16 .1 15 .6 15 .7 14 .8 15 .4 15 .3 16 .4 16 .7 16 .6 Lo g Li ke lih oo d −2 ,9 91 −3 ,0 01 −2 ,9 98 −3 ,0 15 −3 ,0 04 −3 ,0 06 −1 ,8 85 −2 ,9 42 −2 ,9 38 So ur ce : I M F st af f e st im at es . No te : A ll pa ne l e st im at io ns in cl ud e co un try fi xe d ef fe ct s, ti m e fix ed e ffe ct s, a nd b as e ef fe ct s. E st im at io ns a re p er fo rm ed o ve r a c on st an t s am pl e (fo r w hi ch d at a on a ll va ria bl es a re a va ila bl e) . S ta nd ar d er ro rs a re ro bu st e st im a- to rs . F ix ed F X = fix ed e xc ha ng e ra te re gi m e du m m y; H HD = h ou se ho ld d eb t; HH D3 0 = du m m y if ho us eh ol d de bt -to -G DP ra tio e xc ee ds 3 0 pe rc en t; in co m e in eq ua lit y = di ffe re nc e be tw ee n in co m e sh ar e of to p 20 p er ce nt a nd th e bo tto m 2 0 pe rc en t i nc om e gr ou ps ; t ra ns pa re nc y = a du m m y va ria bl e, w he th er a c re di t r eg is try o r o th er fo rm o f b or ro we r i nf or m at io n da ta tr an sp ar en cy e xi st s. ** * p < 0. 01 ; * *p < 0 .0 5; * p < 0. 1. 87 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 credit growth, loan loss provisions, reserve require- ments, liquidity requirements, limits on foreign exchange positions, capital requirements, conservation buffers, leverage ratio, countercyclical capital buffer, limits on foreign currency loans, and taxes on financial institutions) or macroprudential group indices (that is, all MaPPs, loan MaPPs, demand, supply, supply [gen- eral], supply [capital], and supply loans). MaPPs are the cumulative sum of the number of policy changes over the past year (that is, the past four quarters) to reflect the potential delayed effects. A vector of control variables, X i,t , such as real output growth and domestic interest rates, is also included. The model is estimated with quarterly data from 62 countries (32 advanced economies and 30 emerging market economies) from the first quarter of 1990 to the fourth quarter of 2015, using both panel fixed effects and the system gener- alized method of moments technique as outlined by Arellano and Bover (1995). References Aiyagari, S. Rao. 1994. “Uninsured Idiosyncratic Risk and Aggregate Saving.” Quarterly Journal of Economics 109 (3): 659–84. Angeles, Luis. 2015. “Credit Expansion and the Economy.” Applied Economics Letters 22 (13): 1064–72. Arcand, Jean Louis, Enrico Berkes, and Ugo Panizza. 2015. “Too Much Finance?” Journal of Economic Growth 20 (2): 105–48. Arellano, Manuel, and Olympia Bover. 1995. “Another Look at the Instrumental Variable Estimation of Error-Components Models.” Journal of Econometrics 68 (1): 29–51. Arregui, Nicolas, Jaromír Beneš, Ivo Krznar, and Srobona Mitra. 2013. “Evaluating the Net Benefits of Macroprudential Pol- icy: A Cookbook.” IMF Working Paper 13/167, International Monetary Fund, Washington, DC. Badarinza, Cristian, Vimal Balasubramaniam, and Tarun Ramadorai. 2016. “The Indian Household Savings Land- scape.” NCAER Working Paper, National Council of Applied Economic Research, New Delhi, India. Badarinza, Cristian, John Y. Campbell, and Tarun Ramadorai. 2016. “International Comparative Household Finance.” Annual Review of Economics 8 (1): 111–44. Banco Central de Chile. 2017. Financial Stability Report: First Half 2017. Santiago, Chile, June. Bank of Canada. 2015. “Financial System Review.” Ottawa, Ontario, June. ———. 2017. “Financial System Review.” Ottawa, Ontario, June. Bank of England. 2017. “Financial Stability Report.” Issue 41, June. Baron, Matthew, and Wei Xiong. 2017. “Credit Expansion and Neglected Crash Risk.” Quarterly Journal of Eco- nomics 713–64. Barth, James R., Gerard Caprio Jr., and Ross Levine. 2013. “Bank Regulation and Supervision in 180 Countries from 1999 to 2011.” Journal of Financial Economic Policy 5 (2): 111–219. Beck, Thorsten, Berrak Büyükkarabacak, Felix K. Rioja, and Neven T. Valev. 2012. “Who Gets the Credit? And Does It Matter? Household vs. Firm Lending across Countries.” B.E. Journal of Macroeconomics 12 (1): 1–46. Beck, Thorsten, and Ross Levine. 2004. “Stock Markets, Banks, and Growth: Panel Evidence.” Journal of Banking and Finance 28 (3): 423–42. ———, and Norman Loayza. 2000. “Finance and Sources of Growth.” Journal of Financial Economics 58 (1–2): 261–300. Bernanke, Ben S., Mark Gertler, and Simon Gilchrist. 1999. “The Financial Accelerator in a Quantitative Business Cycle Framework.” In Handbook of Macroeconomics, edited by John B. Taylor and Michael Woodford, 1341–93. Amster- dam: Elsevier. Blundell, Richard, Martin Browning, and Costas Meghir. 1994. “Consumer Demand and the Life-Cycle Allocation of House- hold Expenditures.” Review of Economic Studies 61 (1): 57–80. Brunnermeier, Markus, Darius Palia, Karthik Sastry, and Christopher Sims. 2017. “Feedbacks: Financial Markets and Economic Activity.” Working Paper. Brunnermeier, Markus K., and Yuliy Sannikov. 2014. “A Macro- economic Model with a Financial Sector.” American Economic Review 104 (2): 379–421. Calvo, Guillermo A., and Carmen M. Reinhart. 2000. “When Capital Flows Come to a Sudden Stop: Consequences and Policy.” In Reforming the International Monetary and Financial System, edited by Peter B. Kenen and Alexander K. Swoboda. Washington, DC: International Monetary Fund. Calza, Alessandro, Tommaso Monacelli, and Livio Stracca. 2013. “Housing Finance and Monetary Policy.” Journal of the Euro- pean Economic Association 11 (1): 101–22. Carroll, Christopher D. 1992. “The Buffer-Stock Theory of Saving: Some Macroeconomic Evidence.” Brookings Papers on Economic Activity 2, Brookings Institution, Washington, DC. Cerutti, Eugenio, Ricardo Correa, Elisabetta Fiorentino, and Esther Segalla. 2017. “Changes in Prudential Policy Instruments—A New Cross-Country Database.” International Journal of Central Banking 13 (1): 477–503. Cheng, Ing-Haw, Sahíl Raina, and Wei Xiong. 2014. “Wall Street and the Housing Bubble.” American Economic Review 104 (9): 2797–829. Chinn, Menzie D., and Hiro Ito. 2008. “A New Measure of Financial Openness.” Journal of Comparative Policy Analysis 10 (3): 309–22. Coibion, Olivier, Yuriy Gorodnichenko, Marianna Kudlyak, and John Mondragon. 2017. “Does Greater Inequality Lead to 88 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 More Household Borrowing? New Evidence from Household Data.” Federal Reserve Bank of Minneapolis Working Paper 17–04, Minneapolis, MN. Crowe, Christopher, Giovanni Dell’Ariccia, Deniz Igan, and Pau Rabanal. 2013. “How to Deal with Real Estate Booms: Les- sons from Country Experiences.” Journal of Financial Stability 9 (3): 300–19. De Gregorio, Jose. 1996. “Borrowing Constraints, Human Capital Accumulation, and Growth.” Journal of Monetary Economics 37 (1): 49–71. Deaton, Angus. 1991. “Saving and Liquidity Constraints.” Econometrica 59 (5): 1221–48. Demirgüç-Kunt, Asli, and Leora Klapper. 2012. “Measur- ing Financial Inclusion: The Global Findex Database.” Policy Research Working Paper 6025, World Bank, Washington, DC. De Nicolò, Gianni, Giovanni Favara, and Lev Ratnovski. 2012. “Externalities and Macroprudential Policy.” IMF Staff Discussion Note 12/05, International Monetary Fund, Washington, DC. Drehmann, Mathias, and Kostas Tsatsaronis. 2014. “The Credit-to-GDP Gap and Countercyclical Capital Buffers: Questions and Answers.” BIS Quarterly Review (March). Eggertsson, Gauti B., and Paul Krugman. 2012. “Debt, Deleveraging, and the Liquidity Trap: A Fisher-Minsky-Koo Approach.” Quarterly Journal of Economics 127 (3): 1469–513. Elul, Ronel. 2008. “Collateral, Credit History, and the Financial Decelerator.” Journal of Financial Intermediation 17 (1): 63–88. Fisher, Irving. 1933. “The Debt-Deflation Theory of Great Depressions.” Econometrica 1 (4): 337–57. Friedman, Milton. 1957. A Theory of the Consumption Function. Princeton, NJ: Princeton University Press. Fuster, Andreas, David Laibson, and Brock Mendel. 2010. “Nat- ural Expectations and Macroeconomic Fluctuations.” Journal of Economic Perspectives 24 (4): 67–84. Gan, Li, Zhichao Yin, Nan Jia, Shu Xu, Shuang Ma, and Lu Zheng. 2013. Data You Need to Know about China. Research Report of China Household Finance Survey. Heidel- berg: Springer. Geanakoplos, John. 2010. “The Leverage Cycle.” In NBER Macroeconomics Annual 2009, vol. 24, edited by Darron Ace- moglu, Kenneth Rogoff, and Michael Woodford. Cambridge, MA: National Bureau of Economic Research. Gennaioli, Nicola, Andrei Shleifer, and Robert Vishny. 2012. “Neglected Risks, Financial Innovation, and Financial Fragil- ity.” Journal of Financial Economics 104 (3): 452−68. Gourinchas, Pierre-Olivier, and Maurice Obstfeld. 2012. “Stories of The Twentieth Century for the Twenty-First.” American Economic Journal: Macroeconomics 4 (1): 226–65. Hall, Robert E. 1978. “Stochastic Implications of the Life Cycle–Permanent Income Hypothesis: Theory and Evidence.” Journal of Political Economy 86 (6): 971–87. Hanson, Samuel G., Anil K. Kashyap, and Jeremy C. Stein. 2011. “A Macroprudential Approach to Financial Regula- tion.” Journal of Economic Perspectives 25 (1): 3–28. Ilzetzki, Ethan, Carmen M. Reinhart, and Kenneth S. Rogoff. 2017. “Exchange Arrangements Entering the 21st Century: Which Anchor Will Hold?” NBER Working Paper 23134, National Bureau of Economic Research, Cambridge, MA. International Monetary Fund (IMF). 2013. “Key Aspects of Macroprudential Policy—Background Paper.” IMF Policy Paper, Washington, DC. ———. 2016a. “Capital Flows—Review of Experience with the Institutional View.” Washington, DC. ———. 2016b. “Tax Policy, Leverage and Macroeconomic Stability.” Washington, DC. ———. 2017. Canada: Selected Issues and Analytical Notes. IMF Country Report 17/211, Washington, DC. Jácome, Luis I., and Srobona Mitra. 2015. “LTV and DTI Limits—Going Granular.” IMF Working Paper 15/154, International Monetary Fund, Washington DC. Jappelli, Tullio, and Marco Pagano. 1994. “Saving, Growth, and Liquidity Constraints.” Quarterly Journal of Economics 109 (1): 83–109. Jordà, Òscar, Moritz Schularick, and Alan M. Taylor. 2016. “The Great Mortgaging: Housing Finance, Crises and Business Cycles.” Economic Policy 31 (85): 107–52. Kiyotaki, Nobuhiro, and John Moore. 1997. “Credit Cycles.” Journal of Political Economy 105 (2): 211–48. Korinek, Anton, and Alp Simsek. 2016. “Liquidity Trap and Excessive Leverage.” American Economic Review 106 (3): 699–738. Krznar, Ivo, and James Morsink. 2014. “With Great Power Comes Great Responsibility: Macroprudential Tools at Work in Canada.” IMF Working Paper 14/83, International Mone- tary Fund, Washington, DC. Kumhof, Michael, Romain Rancière, and Pablo Winant. 2015. “Inequality, Leverage, and Crises.” American Economic Review 105 (3): 1217–45. Kuttner, Kenneth N., and Ilhyock Shim. 2016. “Can Non-Interest Rate Policies Stabilize Housing Markets? Evidence from a Panel of 57 Economies.” Journal of Financial Stability 26: 31–44. Laeven, Luc, and Fabián Valencia. 2013. “Systemic Banking Crises Database.” IMF Economic Review 61 (2): 225–70. Laibson, David. 1997. “Golden Eggs and Hyperbolic Discount- ing.” Quarterly Journal of Economics 112 (2): 443–78. Levine, Ross. 1998. “The Legal Environment, Banks, and Long-Run Economic Growth.” Journal of Money, Credit and Banking 30 (3): 596–613. Lim, Cheng Hoon, Francesco Columba, Alejo Costa, Piyabha Kongsamut, Akira Otani, Mustafa Saiyid, Torsten Wezel, and Xiaoyong Wu. 2011. “Macroprudential Policy: What Instru- ments and How to Use Them?” IMF Working Paper 11/238, International Monetary Fund, Washington, DC. 89 C H A P T E R 2 h O u S E h O L d d E B T A N d F I N A N C I A L S T A B I L I T Y International Monetary Fund | October 2017 Lombardi, Marco, Madhusudan Mohanty, and Ilhyock Shim. 2017. “The Real Effects of Household Debt in the Short and Long Run.” BIS Working Paper 607, Bank for International Settlements, Basel. Meier, Stephan, and Charles Sprenger. 2010. “Present-Biased Preferences and Credit Card Borrowing.” American Economic Journal: Applied Economics 2 (1): 193–210. Mian, Atif, Kamalesh Rao, and Amir Sufi. 2013. “Household Balance Sheets, Consumption, and the Economic Slump.” Quarterly Journal of Economics 128 (4): 1687–726. Mian, Atif, and Amir Sufi. 2011. “House Prices, Home Equity-Based Borrowing, and the U.S. Household Leverage Crisis.” American Economic Review 101 (5): 2132–56. ———. 2014. House of Debt. Chicago: University of Chicago Press. ———, and Emil Verner. Forthcoming. “Household Debt and Business Cycles Worldwide.” Quarterly Journal of Economics. Mussa, Michael, Giovanni Dell’Ariccia, Barry J. Eichengreen, and Enrica Detragiache. 1998. “Capital Account Liberal- ization: Theoretical and Practical Aspects.” IMF Occasional Paper 172, International Monetary Fund, Washington, DC. Rajan, Raghuram G. 2010. Fault Lines: How Hidden Fractures Still Threaten the World Economy. Princeton, NJ: Princeton University Press. Reserve Bank of Australia. 2017. Financial Stability Review, Sydney, April. Sahay, Ratna, Martin Čihák, Papa N’Diaye, Adolfo Barajas, Srobona Mitra, Annette Kyobe, Yen Nian Mooi, and Seyed Reza Yousefi. 2015a. “Financial Inclusion: Can It Meet Multiple Macroeconomic Goals?” IMF Staff Discussion Note 15/17, International Monetary Fund, Washington DC. Sahay, Ratna, Martin Čihák, Papa N’Diaye, Adolfo Barajas, Ran Bi, Diana Ayala, Yuan Gao, Annette Kyobe, Lam Nguyen, Christian Saborowski, Katsiaryna Svirydenka, and Seyed Reza Yousefi. 2015b. “Rethinking Financial Deepening: Stability and Growth in Emerging Markets.” IMF Staff Discussion Note 15/08, International Monetary Fund, Washington DC. Schmitt-Grohé, Stephanie, and Martín Uribe. 2016. “Down- ward Nominal Wage Rigidity, Currency Pegs, and Invol- untary Unemployment.” Journal of Political Economy 124 (5): 1466–514. Sheedy, Kevin D. 2014. “Debt and Incomplete Financial Markets: A Case for Nominal GDP Targeting.” Brookings Papers on Economic Activity (Spring): 301–72, Brookings Institution, Washington, DC. Shiller, Robert J. 2005. Irrational Exuberance, 2nd ed. Princeton, NJ: Princeton University Press. ———. 2014. “Why Is Housing Finance Still Stuck in Such a Primitive Stage?” American Economic Review: Papers & Proceedings 104 (5): 73–76. South African Reserve Bank. 2017. Financial Stability Review, 1st ed. Pretoria. Stulz, René M. 1999. “Globalization of Equity Markets and the Cost of Capital.” NBER Working Paper 7021, National Bureau of Economic Research, Cambridge, MA. Svirydzenka, Katsiaryna. 2016. “Introducing a New Broad-Based Index of Financial Development.” IMF Working Paper 16/5, International Monetary Fund, Washington, DC. Uribe, Martín, and Stephanie Schmitt-Grohé. 2017. Open Economy Macroeconomics. Princeton, NJ: Princeton Uni- versity Press. Vandenbussche, Jérôme, Ursula Vogel, and Enrica Detragiache. 2015. “Macroprudential Policies and Housing Prices: A New Database and Empirical Evidence for Central, Eastern, and Southeastern Europe.” Journal of Money, Credit and Banking 47 (S1): 343–77. Blank Summary C hanges in the state of the financial system can provide powerful signals about risks to future economic activity. As in the run-up to the global financial crisis, financial vulnerabilities, understood as the extent to which the adverse impact of shocks on economic activity may be amplified by financial frictions, often increase in buoyant economic conditions when funding is widely available and risks appear sub- dued. Once these vulnerabilities are sufficiently elevated, they entail significant downside risks for the economy. Thus, tracking the evolution of financial conditions can provide valuable information for policymakers regarding risks to future growth and, hence, a basis for targeted preemptive action. This chapter develops a new, macroeconomic measure of financial stability by linking financial conditions to the probability distribution of future GDP growth and applying it to a set of major advanced and emerging mar- ket economies. The analytical approach developed in the chapter can be a significant addition to policymakers’ toolkit for macro-financial surveillance. The chapter shows that changes in financial conditions shift the distribution of future GDP growth. While a widening of risk spreads, rising asset price volatility, and waning global risk appetite are sig- nificant predictors of large macroeconomic downturns in the near term, higher leverage and credit growth provide a more significant signal of increased downside risks to GDP growth over the medium term. Thus, at the present juncture, low funding costs and financial market volatility support a sanguine view of risks to the global economy in the near term. But the increasing leverage signals potential risks down the road. A sce- nario of rapid decompression in spreads and an increase in financial market volatility could significantly worsen the risk outlook for global growth. These findings underscore the importance of policymakers maintaining heightened vigilance regarding risks to growth during periods of benign financial conditions that may provide a fertile breed- ing ground for the accumulation of financial vulnerabilities. A retrospective, real-time analysis of the global financial crisis shows that forecasting models augmented with financial conditions would have assigned a considerably higher likelihood to the economic contraction that fol- lowed than those based on recent growth performance alone. Improvements in predictive ability of severe economic contractions, even over short horizons, can be important for timely monetary and crisis-management policies. The ability to harness longer-horizon information from asset prices and credit aggregates can also help in the design of policy rules to address financial vulnerabilities as they develop. The richness of the results obtained across countries suggests that there is significant scope for policymak- ers to further adapt the approach used in this chapter to specific country conditions including, importantly, to reflect structural changes in financial markets and the real economy. FINANCIAL CONDITIONS AND GROWTH AT RISK3CHAPTER 91International Monetary Fund | October 2017 92 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Introduction The global financial crisis was a powerful reminder that financial vulnerabilities can increase both the duration and severity of economic recessions. Finan- cial vulnerabilities, understood as the extent to which the adverse impact of shocks on economic activity may be amplified by financial frictions, usually grow in buoyant economic conditions when investment opportunities seem ample, funding conditions are easy, and risk appetite is high. Once these vulnerabilities are sufficiently high, they can entail significant downside risks for the economy. This interplay between shocks, financial vulnerabil- ities, and growth suggests that financial indicators can provide important intelligence regarding risks to the economic outlook. Policymakers have devoted consid- erable attention to translating the information content of financial indicators into an assessment of financial vulnerability. Approaches that have been used include expert judgment, stress tests, and heatmaps based on multiple early-warning indicators and broad financial conditions indices. These approaches all assess finan- cial vulnerability by linking the state of the financial system to the probability of a financial crisis or bank capital shortage. Because policymakers care about the whole distri- bution of future GDP growth, linking the state of the financial system to such a distribution would enhance macro-financial surveillance. Policymakers would then be able to specify bad outcomes in terms of their risk preferences. For example, it would be possible to calculate the likelihood of output growth being below a given level and to identify thresholds for financial indicators, such as leverage, that signal heightened tail risks to growth. This chapter develops a new analytical tool that maps financial conditions into the probability distribution of future GDP growth. In this chapter, financial conditions correspond to combinations of key domestic financial market asset returns, funding spreads, and volatility; domestic credit aggregates; Prepared by a staff team consisting of Jay Surti (team leader), Mitsuru Katagiri, Romain Lafarguette, Sheheryar Malik, and Dulani Seneviratne, with contributions from Vladimir Pillonca, Aquiles Farias, André Leitão Botelho, Kei Moriya, and Changchun Wang, under the general guidance of Claudio Raddatz and Dong He. The chapter team has benefited from discussions with Norman Swanson, Nellie Liang, and Domenico Giannone. Claudia Cohen and Breanne Rajkumar provided editorial assistance. and external conditions such as measures of global risk sentiment. The methodological approach extends a nascent literature that derives a direct empirical link between financial conditions and risks to the real economy and applies it to 21 major advanced and emerging market economies over the near and medium term. The chapter examines how financial conditions provide information regarding risks to future eco- nomic growth across countries and time horizons. In advanced economies, there may be a stronger associa- tion between financial variables and future economic activity than in emerging market economies because more economic risks are traded in deeper financial markets. But, in both cases, asset prices may remain buoyant until shortly before risks materialize, as the run-up to the global financial crisis showed. Thus, incorporating information on credit aggregates such as leverage into measures of financial conditions may improve forecasts of risks to growth, especially over the medium term. The chapter addresses the following specific questions: • Do changes in financial conditions signal risks to future GDP growth? Are they equally informative for advanced and emerging market economies, about the intensity of recessions and the strength of booms, and over different time horizons? • What types of financial variables are more informa- tive regarding the risks to growth at different time horizons and in different countries? • Could we have used financial conditions to shed light on the likelihood of extremely negative growth outcomes of the past, such as the global recession following the bankruptcy of Leh- man Brothers? • How can policymakers make use of this new tool of macro-financial surveillance? The main findings are as follows: • Changes in a country’s financial conditions shift the distribution of future GDP growth in both advanced and emerging market economies. A tight- ening of financial conditions, reflected in a decom- pression in spreads or an increase in asset price volatility, is a significant predictor of large macro- economic downturns within a one-year horizon. Moreover, in emerging market economies, tighter financial conditions could also portend stronger booms over the subsequent four quarters, possibly because of procyclical capital flows. 93 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 • Asset prices are most informative about risks to growth in the short term, whereas credit aggregates provide more information over longer time horizons. A rising cost of funding and falling asset prices signal a greater threat of severe recession at time horizons of up to four quarters. Higher leverage signals increased downside risk to growth at horizons between one and three years. • Movements in commodity prices and exchange rates affect the real economy in a significant, albeit complex, manner, making a simple economic inter- pretation of their predictive content challenging. On the other hand, a souring of global risk sentiment increases downside risks to growth at short time horizons of one quarter. • In addition to these common patterns, there is heterogeneity in the information content of financial conditions for growth risks across countries. For example, while asset prices are no longer informative over horizons longer than a year for advanced econ- omies, they remain so for emerging markets. • A retrospective real-time analysis of the global finan- cial crisis shows that forecasting models augmented by financial conditions would have assigned a much higher likelihood to the economic contraction that followed than those based on recent growth per- formance alone. The chapter’s approach to linking financial con- ditions and risks to growth can help policymakers in numerous ways. The findings underscore the importance of policymakers maintaining heightened vigilance regarding risks to growth during periods of benign financial conditions that may provide a fertile breeding ground for the accumulation of financial vulnerabilities. Policymakers may respond to signals of an imminent near-term dire economic outcome with crisis-management-type discretionary policy actions that encompass a range of monetary and macropruden- tial tools. More broadly, this also helps in the design of policy rules to address financial vulnerabilities as they develop through the introduction of appropriate countercyclical macroprudential tools. In this regard, the output of the forecasting models could be used to calibrate parameters of structural macro-financial models used to guide such policy.1 The richness of the 1Just as estimated vector autoregression models have been used to calibrate the parameters of linear dynamic general equilibrium models used to pin down optimal monetary policy rules (for example, Christiano, Eichenbaum, and Evans 2005; Del Negro and Schorfheide 2009). results obtained across countries suggests that there is significant scope for authorities to further adapt the broad approach used in this chapter to specific country conditions, including, importantly, to reflect structural changes in financial markets and the real economy. The rest of this chapter is organized as follows. The next section discusses conceptual issues related to the links between macro-financial conditions, financial vulnerabilities, and risks to the outlook for economic growth. The subsequent section looks at how asset prices and financial aggregates combine to signal short- to medium-term risks to future GDP growth. The section after that provides an empirical assessment of the degree to which the information contained in measures of financial conditions can help forecast risks to economic growth in major advanced and emerging market economies over horizons up to one year. The final section discusses policy implications. Annexes explain the potential policy applications, construc- tion of financial conditions, and modeling of risks to growth in more detail. Financial Conditions and Risks to Growth: Conceptual Issues Economic growth has a complex and nonlinear relationship with shocks and financial vulnerabilities. Theory and recent experience both support the view that financial vulnerabilities increase risks to growth.2 When investment opportunities seem abundant and the means of financing them are easily and cheaply available, financial vulnerabilities tend to increase. Once such vulnerabilities are sufficiently high, they can amplify and prolong the impact of shocks on economic activity. GDP growth responds nonlinearly to shocks in the presence of financial vulnerabilities, which increases the likelihood of severely negative economic outcomes.3 Under such circumstances, assessments of both the baseline growth outlook and the risks to such an outlook are informed not only by the span and severity of relevant risk factors that are the source of shocks, but also by the intelligence provided by the interplay of factors that increase financial vulnerability. 2Empirical evidence shows that recessions accompanied by financial crises are typically much more severe and protracted than ordinary recessions (Claessens, Kose, and Terrones 2011a, 2011b). 3Annex 3.1 provides a framework for understanding the joint dynamics of financial vulnerabilities and growth risks in a structural macro model. 94 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Several factors cause financial vulnerabilities to grow in a buoyant macro-financial environment. Ease of borrowing and high asset prices reduce the incentives to manage liquidity and solvency risks. Perceptions of high investment returns relative to the cost of funding and of the improved quality of collateral incentivize households and firms to increase their leverage without taking into account the potential negative externali- ties resulting from their collective borrowing decisions (Bianchi 2011; Korinek and Simsek 2016; Bianchi and Mendoza, forthcoming). Booming asset prices also boost the capital adequacy, lending capacity, and risk appetite of financial intermediaries (Brunnermeier and Pedersen 2009; Adrian, Moench, and Shin 2010; Adrian and Shin 2014). As intermediaries respond by increas- ing short-term wholesale funding to finance long-term credit exposures, maturity mismatches and other balance sheet weaknesses accumulate in the financial sector. For example, lenders’ incentives to invest in costly under- writing are reduced, which can result in significant mispricing of credit risk (Gorton and Ordoñez 2014). The need to lower significant debt and correct balance sheet mismatches can clog financial interme- diation, investment, and growth for a long time once the credit cycle turns. With vulnerabilities substan- tially elevated, even small negative shocks can cause significant reversals because they force lenders to face up to the true quality of exposures and collateral. This results in a significant tightening in credit conditions. Some firms and households may be forced into default, while others may have to liquidate assets. The ensuing pressure on lenders’ profits and collateral values can then generate further rounds of contraction in credit, investment, and growth. In addition to the direct nega- tive impact of these events on lenders’ profits, rising volatility and risk spreads constrain lenders’ capacity to bear risk by increasing the capital required as a buffer against existing exposures (He and Krishnamurthy 2013; Brunnermeier and Sannikov 2014). In such cir- cumstances, risk-bearing capacity will be affected not only by capital constraints but also by funding liquid- ity concerns (Gertler, Kiyotaki, and Prestipino 2017). A large body of empirical work has examined the information content of asset prices in forecasting the baseline growth outlook.4 Various asset prices have been found to be useful predictors of future output growth in 4Stock and Watson (2003) produce a comprehensive survey of the literature up to the early 2000s. some countries and in some periods. Combining fore- casts obtained from models with individual asset prices appears to result in more consistent, higher-quality fore- casts. Short-term yields on risk-free securities and term spreads capture the stance of monetary policy and there- fore contain useful information about future economic activity (Laurent 1988; Estrella and Hardouvelis 1991; Bernanke and Blinder 1992; Estrella and Mishkin 1998; Ang, Piazzesi, and Wei 2006). Corporate bond spreads signal changes in the default-adjusted marginal return on business fixed investment (Philippon 2009) and shocks to the profitability and creditworthiness of financial intermediaries (Gilchrist and Zakrajšek 2012).5 There is some evidence that elevated stock-return volatility can be a useful predictor of output contraction over short horizons (Campbell and others 2001), although empiri- cal evidence for the predictive content of stock returns is weak (Campbell 1999; Stock and Watson 2003). The key departure of this chapter is to focus on the information content of financial indicators in forecast- ing risks to growth. In addition to asset prices, credit aggregates can also be expected to provide information on the risks to growth in the short, medium, and long term. For example, a combination of low leverage and buoyant asset prices is likely to correspond, over the short term, to high expected growth (an optimistic baseline outlook) and a low likelihood of adverse out- comes (sanguine risk outlook as represented, poten- tially, by a probability density of short-term growth with relatively low variance). On the other hand, theory suggests that such an environment might be ideal for a buildup of vulnerabilities over the medium term, ultimately increasing the likelihood of low growth outcomes. As such a possibility becomes more certain, spreads and market volatility would rise and asset prices would fall.6 Other financial variables can 5Gilchrist and Zakrajšek (2012) demonstrate the superiority of their constructed bond spread over alternative proxies for the default spread investigated in the earlier literature; for example, the Baa-Aaa bond spread (Bernanke 1983), the commercial paper–Treasury bill spread (Stock and Watson 1989; Friedman and Kuttner 1998), and the so-called junk bond spread (Gertler and Lown 1999). 6Financial indicators can be classified into two types. Fast-moving asset prices tend to signal risks to growth over the near term, whereas balance sheet aggregates change gradually over time and may indicate risks over longer horizons. The evolution of aggregates and prices is not by any means independent. For example, the growth in aggregates may, beyond a point, change market expectations of risks. This would be reflected in tightening spreads, which then signal risks to growth in the near term. For a discussion, see Adrian and Liang 2016 and Krishnamurthy and Muir 2016. 95 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 also be very informative in the context of small open advanced economies and emerging market economies. These variables include the nominal exchange rate and commodity prices, which may affect the cost of external funding and the availability of international collateral (Caballero and Krishnamurthy 2006). This chapter refers to such a combination of finan- cial indicators, or an index constituted of them, as financial conditions. The term “financial conditions” often refers to the ease of funding (Chapter 3 of the April 2017 Global Financial Stability Report [GFSR]), but here it is used to refer to the combination of a broad set of financial variables that influence economic behavior and thereby the future of the economy.7 This chapter examines two alternative approaches to constructing measures of financial conditions from the information contained in several financial indicators. One attractive option is a single financial conditions index (FCI). An important advantage of such a univar- iate FCI is the parsimony with which it aggregates the information content of multiple financial indicators. Parsimony is highly desirable for forecasting because it reduces parameter uncertainty, but it may lead to suppressing the information provided by certain variables by commingling them with other, more volatile indicators in a single index. For example, the higher variability of asset prices and risk spreads may lead them to dominate univariate FCIs, with credit aggregates being assigned small factor loadings (as is indeed the case in the application described below). Since credit aggregates may carry significant infor- mation about risks to growth at longer horizons, the chapter pursues a second approach wherein financial indicators are partitioned into three separate groups based on economic similarity. The three subindices are the domestic price of risk (risk spreads, asset returns, and price volatility), credit aggregates (leverage and credit growth), and external conditions (global risk sentiment, commodity prices, and exchange rates). The separation of a large set of financial indicators into these three predetermined categories is a reasonable compromise between maintaining parsimony, allowing various classes of indicators to provide separate signals about risks to growth at different horizons, and being able to provide a more direct economic interpretation of the various subindices. 7This notion of financial conditions is similar to the definition proposed by Hatzius and others (2010). See Annex 3.2 for details on the construction of financial conditions used in this chapter. The chapter’s empirical framework is centered on forecasts of the probability distribution of future growth outcomes based on financial conditions in a way that allows for nonlinearity and state dependence. Building on the literature on conditional density fore- casting and recent research on forecasting the distribu- tion of growth in the United States, the chapter uses financial conditions to forecast the probability distri- bution of future GDP growth in major advanced and emerging market economies for horizons of up to three years through quantile projections.8 The flexibility of this approach captures the rich nonlinear interaction between shocks, financial vulnerabilities, and economic outcomes predicted by theory. For instance, consider two combinations of financial indicators that forecast the same future median growth rate. The first combi- nation forecasts much greater downside growth risk (that is, a probability density with a significantly fatter left tail) than the second. This indicates that for a con- stant distribution of fundamental shocks, the economy is more likely to experience a very bad economic out- come in the future under the first configuration than under the second. In this sense, the first combination signals a financial system that is more vulnerable. These density forecasts can subsequently be exploited to con- struct measures of risks to economic growth associated with the state of the financial system. Such an approach provides a natural way of assessing financial vulnerability that has several distinct advan- tages. First, the estimated link between financial condi- tions and the distribution of future economic activity would provide a close measure of financial vulnera- bility, understood as the extent to which the financial system amplifies shocks. Second, to the extent that pol- icymakers care about the whole distribution of future GDP growth, it provides a complete depiction of the risks to economic activity associated with the state of the financial system. Third, it allows policymakers to define risk tolerance in terms of GDP growth, which is more general than in terms of the probability of a financial crisis as defined under specific criteria or another ad hoc metric. For instance, this approach gives precise answers to questions such as the probabil- 8See Annex 3.3 for details on the empirical framework. Con- ditional density forecasting is surveyed by Tay and Wallis (2000); Corradi and Swanson (2006); and Komunjer (2013). The chapter’s methodology builds on some recent studies (Adrian, Boyarchenko, and Giannone 2016; De Nicolò and Lucchetta 2017) that establish a direct empirical link between financial conditions and risks to economic growth. 96 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 ity of GDP growth being less than –3 percent one year ahead given the current—or any hypothetical—state of the financial system. How Do Changes in Financial Conditions Indicate Risks to Growth? Over a horizon of one to four quarters, tighter finan- cial conditions—as reflected in higher univariate FCIs—predict increased downside risks to GDP growth in most advanced economies and a more uncertain growth outlook in several emerging market economies. An increasing domestic price of risk signals an elevated threat of imminent, severe recession in advanced and emerging market economies. Rising leverage is a sig- nificant predictor of elevated downside risk over the medium term. Country-specific results vary considerably, suggesting a rich interplay of the drivers of growth risk. What Underpins Economies’ Financial Conditions Indices?9 The drivers of economies’ FCIs vary considerably across a sample of major advanced and emerging mar- ket economies.10 An increase in the FCI corresponds to tighter financial conditions, that is, higher spreads and volatility, lower asset prices, worsening risk sentiment, exchange rate depreciation, and unfavorable commod- ity price movements. Beyond this common finding, the relative importance of these factors in determining the evolution of FCIs varies considerably across coun- tries. Higher corporate funding costs and worsening global risk sentiment (as captured by rising levels of the Chicago Board Options Exchange Volatility Index [VIX] and Merrill Lynch Option Volatility Estimate [MOVE] Index) tighten financial conditions across the board. But while sovereign spreads are clearly import- ant in emerging market economies, they are rarely so in advanced economies. And while increasing com- modity prices loosen financial conditions in exporters such as Australia, Brazil, Canada, Chile, and Russia, they tighten them in commodity-importing countries. Exchange rate appreciation uniformly loosens financial 9In this subsection, financial conditions reference the univariate FCIs described in the preceding section. 10The financial indicators that constitute a country’s FCIs may evolve over time for many reasons, including changes in risk appetite or investor risk sentiment. The methodology used to construct the FCIs, the list of financial indicators, and the sample of countries are described in detail in Annex 3.2. conditions.11 In the case of emerging market and small open economies, this may reflect the correspondence of an appreciating exchange rate with strong capital inflows. In general, asset price shocks appear to be more important in driving changes in FCIs than credit aggregates. This pattern, however, may reflect the slower speed at which credit adjusts relative to changes in GDP at turning points in the economic cycle, especially at the end of economic booms preceding financial crises. What Information Do Univariate FCIs Convey about Future Growth? An increase in the FCI would signal higher down- side risks in both advanced and emerging market economies. An increase in the global FCI signals heightened downside risk to world GDP growth (Figure 3.1).12 Movements in the FCI are especially powerful signals of changes in downside tail risk to the global economy but are less informative about the baseline growth outlook and the strength of economic booms. This is reflected in the fact that the forecast of the left tail of the distribution of global GDP growth decreases significantly in response to an increase in the FCI both one quarter and four quarters ahead. In contrast, the forecasts of the central tendency of GDP growth (as captured by the median growth rate) and of the strength of booms (at the right tail of the growth distribution forecasts) are considerably less responsive to changes in the FCI, and their movement is apparent only for large changes in the FCI such as those observed in the global financial crisis. This is also the case for individual countries—the forecasts of the worst-case outcomes (at the 5th percentile of the future GDP growth distribution) are between 3 times (United States) and more than 10 times (Australia) more sensi- 11Exchange rate movements may reflect a complex combination of factors. With respect to a country’s FCI, changes in the exchange rate are most likely to be associated with changes in the ease of exter- nal financing conditions, which may relate either to evolving global funding conditions and risk sentiment or changes in the market’s perception of the country’s creditworthiness or both. Exchange rate depreciations are, in such an association, a reflection of a worsening of global conditions or in market perceptions of a country’s risk profile. Empirically, such an association appears to apply to most countries covered in the chapter, although the link has been noted in the literature as relevant primarily for emerging market economies. 12The global FCI is defined as the first principal component of the country-level FCIs. 97 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 tive to changes in FCIs than the forecasts of the central tendency of economic growth. Easing of global financial conditions through 2016 signaled reduced tail risk to global growth for 2017. This is evident in the upward movement in the bottom tail of the GDP growth density forecast (5th percen- tile) for the world economy (Figure 3.1) and a similar movement in several countries, including Australia, Brazil, South Africa, Turkey, and the United States (Figure 3.2).13 Nonetheless, FCIs do carry significant information regarding upside risks to future economic growth for emerging markets (Figure 3.3). In Brazil, Korea, and Mexico, higher levels of the FCI portend a more uncertain growth outlook at a one-year horizon, as reflected in coefficients of opposite signs at the lowest and highest quantiles (which imply fatter and longer tails at both ends of the distribution of future GDP growth). In some commodity-exporting countries, such as Chile, tightening FCIs appear to signal risk of stronger recessions as well as economic booms of lower intensity (Figure 3.3, panel 2). Different properties of advanced and emerging market economy business cycles may account for the differing significance of the information provided by changing FCIs across countries. Some emerging market economies and commodity exporters may have a more pronounced and symmetrical boom-bust cycle that is closely tied to export-commodity prices and global risk sentiment. Positive developments in either factor can motivate significant capital inflows, relaxing domestic financial constraints on growth.14 When the risk envi- ronment reverses, capital flows may retrench, exchange rates can depreciate, and investment and growth can decline (Aguiar and Gopinath 2007). This may explain why a tightening of financial conditions can move the density of GDP growth to the left (Figure 3.3, panel 2). More broadly, increases in FCIs in emerging market economies may reflect domestic interest rate hikes targeted at attenuating overheating due to high domes- tic demand. But the higher interest rates may attract 13The exact magnitude of the movements can be improved by further country-specific calibration that, for instance, increases the number of financial indicators used in FCI construction, but the direction of the movements indicated by the model is quite robust and showcases the potential of this methodology. 14For the role of commodity prices in explaining the cyclical movements of capital flows to emerging market economies, see, for example, Chapter 4 of the April 2017 Regional Economic Outlook for the Western Hemisphere. –2.0 –1.8 –1.6 –1.4 –1.2 –1.0 –0.8 –0.6 –0.4 –0.2 0.0 5th 25th 50th 75th 95th Percentile –10 0 10 20 30 40 –20 –15 –10 –5 0 5 10 19 91 :Q 1 93 :Q 1 95 :Q 1 97 :Q 1 99 :Q 1 20 01 :Q 1 03 :Q 1 05 :Q 1 07 :Q 1 09 :Q 1 11 :Q 1 13 :Q 1 15 :Q 1 Downside and upside risks (5th and 95th percentiles) Median FCI (right scale) 16 :Q 4 Sources: Bloomberg Finance L.P.; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: Panel 1 depicts the estimated coefficients on the current quarter FCI in a quantile regression of four-quarters-ahead GDP growth on current quarter FCI and GDP growth. Panel 2 depicts the time series of estimated, conditional 5th, 50th, and 95th quantiles of four-quarters-ahead GDP growth. The median (red) line denotes the forecast of the 50th quantile of GDP growth made four quarters earlier using the methodology described in Annex 3.3. The shaded area is bound at the top and bottom by, respectively, the forecasts of the 95th and 5th quantiles of GDP growth made four quarters earlier. FCI = financial conditions index. Figure 3.1. Tighter Financial Conditions Forecast Greater Downside Tail Risk to Global Growth 1. Quantile Coefficient Estimates (Standard deviations) As financial conditions tighten, the probability of a large economic contraction increases ... 2. One-Year-Ahead Density Forecast (Left scale = percent; right scale = standard deviations) ... as was seen in the recent global financial and euro area sovereign debt crises. 98 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 –9 –6 –3 0 3 6 9 –15 –10 –5 0 5 10 15 19 91 :Q 1 93 :Q 1 95 :Q 1 97 :Q 1 99 :Q 1 20 01 :Q 1 03 :Q 1 05 :Q 1 07 :Q 1 09 :Q 1 11 :Q 1 13 :Q 1 15 :Q 1 16 :Q 4 –10 –5 0 5 10 15 –6 –4 –2 0 2 4 6 8 19 73 :Q 1 75 :Q 1 77 :Q 1 79 :Q 1 81 :Q 1 83 :Q 1 85 :Q 1 87 :Q 1 89 :Q 1 91 :Q 1 93 :Q 1 95 :Q 1 97 :Q 1 99 :Q 1 20 01 :Q 1 03 :Q 1 05 :Q 1 07 :Q 1 09 :Q 1 11 :Q 1 13 :Q 1 15 :Q 1 16 :Q 4 –5 0 5 10 15 –8 –4 0 4 8 19 91 :Q 1 93 :Q 1 95 :Q 1 97 :Q 1 99 :Q 1 20 01 :Q 1 03 :Q 1 05 :Q 1 07 :Q 1 09 :Q 1 11 :Q 1 13 :Q 1 15 :Q 1 16 :Q 4 –8 –4 0 4 8 12 –8 –4 0 4 8 19 81 :Q 1 83 :Q 1 85 :Q 1 87 :Q 1 89 :Q 1 91 :Q 1 93 :Q 1 95 :Q 1 97 :Q 1 99 :Q 1 20 01 :Q 1 03 :Q 1 05 :Q 1 07 :Q 1 09 :Q 1 11 :Q 1 13 :Q 1 15 :Q 1 16 :Q 4 –4 0 4 8 12 –20 –10 0 10 20 19 91 :Q 1 93 :Q 1 95 :Q 1 97 :Q 1 99 :Q 1 20 01 :Q 1 03 :Q 1 05 :Q 1 07 :Q 1 09 :Q 1 11 :Q 1 13 :Q 1 15 :Q 1 16 :Q 4 –18 –12 –6 0 6 12 18 –10 –5 0 5 10 19 73 :Q 1 75 :Q 1 77 :Q 1 79 :Q 1 81 :Q 1 83 :Q 1 85 :Q 1 87 :Q 1 89 :Q 1 91 :Q 1 93 :Q 1 95 :Q 1 97 :Q 1 99 :Q 1 20 01 :Q 1 03 :Q 1 05 :Q 1 07 :Q 1 09 :Q 1 11 :Q 1 13 :Q 1 15 :Q 1 16 :Q 4 Downside and upside risks (5th and 95th percentiles) Median FCI (right scale) Sources: Bloomberg Finance L.P.; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: The country-specific financial conditions indices (FCIs) are constructed using the methodology described in Annex 3.2. The median (red) line at each point in time denotes the forecast of the 50th quantile of GDP growth made four quarters earlier using the methodology described in Annex 3.3. The shaded area is bound at the top and bottom by, respectively, the forecasts of the 95th and 5th quantiles of GDP growth made four quarters earlier. Figure 3.2. Risk of Severe Recessions Is Especially Sensitive to a Tightening of Financial Conditions in Major Advanced and Emerging Market Economies (One-year-ahead density forecasts; left scale = percent; right scale = standard deviations) 1. Brazil 2. Australia 3. South Africa 4. Sweden 5. Turkey 6. United States 99 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 capital inflows and thereby extend ongoing credit and economic booms. This may explain why tightening of financial conditions appears to be a good indicator of growing positive and negative risks around the baseline (Figure 3.3, panels 1, 3–4). Which Asset Prices and Aggregates Best Signal Growth Risks at Various Time Horizons? Asset prices are differentially informative regarding the domestic price of risk across countries. Term and interbank spreads, followed by corporate and sovereign spreads, are the most important risk indicators for the investment and growth outlook across advanced economies. The dynamics of house prices are particu- larly important in countries where either the share of homeownership and floating-rate mortgages is high (such as the United Kingdom) or the mortgage market is a key node that underpins pricing and activity in systemic funding markets (as in the United States). The evidence for emerging market economies is more challenging to interpret for two reasons. First, data are much more limited and are available only for more recent years. Second, in many countries, financial mar- ket activity is often focused on equity and government bond markets. Unsurprisingly, therefore, analysis of available data suggests that for these countries, sover- eign spreads and equity returns are most significant. Domestic asset prices are the dominant driver of growth risks in the short term, while credit aggregates –0.8 –0.6 –0.4 –0.2 0.0 0.2 0.4 0.6 –1.6 –1.4 –1.2 –1.0 –0.8 –0.6 –0.4 –0.2 0.0 5th 25th 50th 75th 95th Percentile –2.5 –2.0 –1.5 –1.0 –0.5 0.0 0.5 1.0 5th 25th 50th 75th 95th Percentile 5th 25th 50th 75th 95th Percentile –1.0 –0.5 0.0 0.5 1.0 1.5 5th 25th 50th 75th 95th Percentile Sources: Bloomberg Finance L.P.; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: The panels depict estimated coefficients on the current quarter financial conditions index (FCI) from quantile regressions of four-quarters-ahead GDP growth on current quarter FCI and GDP growth. The coefficients are standardized to depict the impact of a one standard deviation increase in current quarter FCIs on four-quarters-ahead GDP growth (also expressed in standard deviations). 1In line with Morgan Stanley Capital International (MSCI) markets classification criteria, Korea is classified as an emerging market economy in panel 4. Figure 3.3. In Emerging Market Economies, Changes in Financial Conditions Also Affect Upside Risks (Quantile regression estimates for selected emerging market economies: four quarters ahead) 1. Brazil 2. Chile 3. Mexico 4. Korea1 100 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 are the dominant drivers in the medium term. Results from a panel quantile regression with country fixed effects, estimated separately for advanced and emerging market economies, highlight some common patterns in the relationship between these FCI components and risks to growth. • Domestic price of risk: Tightening of financial condi- tions caused by a rising price of risk is a significant predictor of downside growth risks over horizons of up to one year. This inverse relationship between the price of risk and the growth forecast is stronger in the left tail of the distribution of future growth and is more significant for advanced economies (Figure 3.4, panels 1–4). The price of risk becomes uninformative over longer horizons in advanced economies. In emerging market economies, an inter- esting pattern arises—a higher price of risk signals lower downside (tail) risks at two- to three-year horizons. One possible explanation is the negative impact of tighter domestic financial conditions on leverage and balance sheet expansion, which appears to be associated with lower risks to growth in both the short and medium term (Figure 3.4, panels 5–6). • Leverage: Higher credit growth and credit to GDP signal greater downside risk to growth at horizons of one year and longer. The relationship is eco- nomically more significant at the lower quantiles of GDP growth and in advanced economies than in emerging market economies (Figure 3.5, panels 1–2). Over shorter time horizons (one quarter), however, the information content differs across countries, with rising leverage continuing to signal higher downside risks in emerging market and large advanced economies but signaling lower downside risks in small open advanced economies. • External conditions: While changing external con- ditions convey statistically significant information regarding risks to future growth, their informa- tion content represents a complex combination of forces. For example, movements in exchange rates can reflect different risk implications through real and financial channels, each of which may be more potent at different horizons. And the impact of changes in commodity prices on risks to growth will differ depending on whether a country is a commodity exporter or importer. Consequently, the signal given by changes in external conditions proved difficult to interpret in a straightforward manner. Nonetheless, a clearer interpretation arises when isolating changes in global risk sentiment from the other external variables.15 Higher global risk aversion, reflected in a higher VIX, signals greater downside risks to growth in the short term, includ- ing a larger threat of an imminent recession (Fig- ure 3.6). However, increases in the VIX also signal lower downside risks to growth at longer horizons of one to two years, possibly because, in most cases, tighter global financial conditions slow the growth of leverage and balance sheet mismatches, which may lessen medium-term growth risks. The view that emerges from these results is that the prevailing low funding costs and financial market volatility support a positive view of risks to the global economy in the short term, but increasing lever- age signals potential risks down the road. In such circumstances, a scenario of a rapid decompression in spreads and increase in financial market volatil- ity could significantly worsen the risk outlook for global growth. How Well Do Changes in Financial Conditions Forecast Downside Risks to Growth? Severely adverse growth performance during the global financial crisis is used to demonstrate the potential use of measures of financial conditions in improv- ing forecasts of risks to growth at horizons of up to one year. Augmenting growth forecast models based on past growth performance with financial condi- tions significantly improves forecasting ability. This is reflected in the greater likelihood that is assigned to the actual negative growth outcomes during that period. Applying the univariate FCIs to historical episodes highlights the index’s power to help predict future economic downturns over short horizons. Notably, the model was used to predict the distribution of growth for the first quarter of 2009, broadly corresponding to the peak of the global financial crisis. • At a one-quarter horizon (that is, in the fourth quarter of 2008), conditioning the risk forecast of future growth on financial conditions (besides economic growth) adds significantly to capturing 15Formally, a separate model of the kind described in Annex 3.2 was examined with the external conditions subindex defined as a global risk sentiment index (equal to the change in the VIX). 101 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 –0.4 –0.3 –0.2 –0.1 0.0 0.1 0.2 0.10 0.20 0.25 0.40 0.50 0.60 0.75 0.80 0.90 Quantile –0.4 –0.3 –0.2 –0.1 0.0 0.1 0.2 –0.4 –0.3 –0.2 –0.1 0.0 0.1 0.2 0.10 0.20 0.25 0.40 0.50 0.60 0.75 0.80 0.90 Quantile –0.4 –0.3 –0.2 –0.1 0.0 0.1 0.2 0.10 0.20 0.25 0.40 0.50 0.60 0.75 0.80 0.90 Quantile 0.10 0.20 0.25 0.40 0.50 0.60 0.75 0.80 0.90 Quantile –0.1 0.0 0.1 0.2 0.10 0.20 0.25 0.40 0.50 0.60 0.75 0.80 0.90 Quantile –0.1 0.0 0.1 0.2 0.10 0.20 0.25 0.40 0.50 0.60 0.75 0.80 0.90 Quantile Sources: Bloomberg Finance L.P.; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: The panels depict coefficient estimates on the price of risk index in pooled quantile regressions of one-quarter-ahead, four-quarters-ahead, and eight-quarters- ahead GDP growth for advanced economies (left column) and emerging market economies (right column). The coefficients are standardized by centering and reducing (zero mean, unit variance) both the dependent variable and the regressors to enable comparison across quantiles, across time horizons, and between advanced and emerging market economies. The coefficient estimate for a given quantile should be read as the impact of a one standard deviation change in the price of risk on the future quantile of GDP growth also expressed in terms of standard deviations. The vertical lines in the green bars denote confidence intervals at 10 percent and, where they cross the x-axis, correspond to absence of statistical significance of the regressor. Figure 3.4. Higher Price of Risk Is a Significant Predictor of Downside Growth Risks within One Year (Quantile regression coefficients) 1. Advanced Economies: One Quarter Ahead Economic significance is highest over one quarter ... 2. Emerging Market Economies: One Quarter Ahead ... albeit less so in emerging market economies. 3. Advanced Economies: One Year Ahead It remains so over one year in advanced economies ... 4. Emerging Market Economies: One Year Ahead ... and in emerging market economies. 5. Advanced Economies: Two Years Ahead Price of risk becomes uninformative over longer horizons in advanced economies ... 6. Emerging Market Economies: Two Years Ahead ... but, in emerging market economies, higher funding costs signal lower risk over longer horizons. 102 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 imminent tail risks to growth, both at the epicenter of the crisis (that is, the United States) and in a commodity-exporting emerging market economy (Chile). Notably, the likelihood attached to poor growth outcomes around the actual realization is significantly higher if rapidly tightening financial conditions are incorporated into the growth forecast (the density in red) as opposed to a model whose only information for forecasting is the growth outcome (the density in blue) in the fourth quarter of 2008 (Figure 3.7).16 16GDP growth exhibits a high degree of persistence in the sample of advanced and emerging market economies covered by this chap- ter’s analysis. Consequently, from a forecasting perspective, a quantile autoregression model of GDP growth represents a conservative and hard-to-beat benchmark against which to assess the marginal con- ditioning information content of financial conditions. The quantile autoregression model is unlikely to forecast rare (severe) recessions –0.4 –0.3 –0.2 –0.1 0.0 0.1 0.2 0.10 0.20 0.25 0.40 0.50 0.60 0.75 0.80 0.90 Quantile –0.4 –0.3 –0.2 –0.1 0.0 0.1 0.2 0.10 0.20 0.25 0.40 0.50 0.60 0.75 0.80 0.90 Quantile Sources: Bloomberg Finance L.P.; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: The panels depict coefficient estimates on the credit aggregates index in pooled quantile regressions of three-years-ahead GDP growth for advanced and emerging market economies. The coefficients are standardized by centering and reducing (zero mean, unit variance) both the dependent variable and the regressors to enable comparison across quantiles, across time horizons, and between advanced and emerging market economies. The coefficient estimate for a given quantile should be read as the impact of a one standard deviation change in leverage on the future quantile of GDP growth also expressed in terms of standard deviations. The vertical lines in the green bars denote confidence intervals at 10 percent and, where they cross the x-axis, correspond to absence of statistical significance of the regressor. Figure 3.5. Rising Leverage Signals Higher Downside Growth Risks at Longer Time Horizons (Quantile regression coefficients) 1. Advanced Economies: Three Years Ahead 2. Emerging Market Economies: Three Years Ahead –0.4 –0.3 –0.2 –0.1 0.0 0.1 0.2 0.10 0.20 0.25 0.40 0.50 0.60 0.75 0.80 0.90 Quantile –0.4 –0.3 –0.2 –0.1 0.0 0.1 0.2 0.10 0.20 0.25 0.40 0.50 0.60 0.75 0.80 0.90 Quantile Sources: Bloomberg Finance L.P.; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: The panels depict coefficient estimates on the VIX index in pooled quantile regressions of one-quarter-ahead GDP growth for advanced and emerging market economies. The coefficients are standardized by centering and reducing (zero mean, unit variance) both the dependent variable and the regressors to enable comparison across quantiles, across time horizons, and between advanced and emerging market economies. The coefficient estimate for a given quantile should be read as the impact of a one standard deviation change in the VIX on the future quantile of GDP growth also expressed in terms of standard deviations. The vertical lines in the green bars denote confidence intervals at 10 percent and, where they cross the x-axis, correspond to absence of statistical significance of the regressor. VIX = Chicago Board Options Exchange Volatility Index. Figure 3.6. Waning Global Risk Appetite Signals Imminent Downside Risks to Growth (Quantile regression coefficients) 1. Advanced Economies: One Quarter Ahead (External conditions = VIX) 2. Emerging Market Economies: One Quarter Ahead (External conditions = VIX) 103 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 • These results remain robust in a broader cross sec- tion of countries. Among countries that experienced a significant growth downturn during the crisis, adding FCIs to an autoregressive growth forecast- ing model significantly increases the conditional likelihood of a GDP growth outcome less than or equal to the actual growth outturn one quarter ahead (Table 3.1).17 In addition to predicting a fat- ter left tail for the growth distribution, the average growth forecasts including FCIs are closer to the actual severe economic contraction experienced by these countries in the first quarter of 2009, and well below the market consensus, which remained rela- tively optimistic even after the collapse of Lehman Brothers (Table 3.2). The exercise also shows that conditioning on uni- variate FCIs may not work as well at longer horizons. This possibility is evident when comparing the relative predictive ability of the autoregressive growth model with the model augmented with FCIs at one- and four-quarter horizons for the first quarter of 2009. In the case of the global financial crisis, examining the behavior of sampled countries’ FCIs through 2008 is revealing. Close examination shows why the forecast- ing gain differs once the information set is augmented with FCIs at different time horizons. In the first quarter of 2009, GDP growth for most countries was among the worst in their recent economic history. The Lehman Brothers bankruptcy, at the beginning of the fourth quarter of 2008, was the bellwether for a swift and severe deterioration in financial conditions. Risk spreads and market volatility increased steeply, and asset values crashed. The information emanating from FCIs throughout the fourth quarter of 2008 clearly sig- naled potential negative fallout for economic activity. By contrast, economic indicators took additional time to catch up to the actual magnitude of the decline. and macroeconomic crises well. A good test of the predictive contribution of financial indicators for such growth episodes would be to examine how their addition to the conditioning information set would change the likelihood assigned to the realized (bad) growth outcome at various horizons. 17Results are presented for a selection of advanced and emerging market economies in Tables 3.1–3.3, even though similar results are obtained for other sampled countries that experienced a recession at the time of the global financial crisis. Results for countries that did not experience an economic contraction suggest that the model augmented with FCIs does not generate false alarms—that is, significantly lower conditional probability of a recession at one- and four-quarter forecast horizons. 0.05 0.10 0.00 0.00 0.15 0.20 –15.0 –12.5 –10.0 –7.5 –5.0 –2.5 0.0 2.5 5.0 7.5 10.0 12.5 15.0 0.02 0.04 0.06 0.08 0.10 0.12 0.14 –15.0 –12.5 –10.0 –7.5 –5.0 –2.5 0.0 2.5 5.0 7.5 10.0 12.5 15.0 GDP growth (quarter-over-quarter annualized percent change) GDP growth (quarter-over-quarter annualized percent change) One-quarter-ahead conditional forecast density (at 2008:Q4): without FCI One-quarter-ahead conditional forecast density (at 2008:Q4): with FCI Realized value Sources: Bloomberg Finance L.P.; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: The figure displays conditional probability distributions of one-quarter-ahead GDP growth based on a parametric, T-skew density, fitted over quantile regression estimates as described in Annex 3.3. In particular, it includes two conditional distributions of growth based on two forecasting models that use either growth or growth and financial conditions indices (FCIs) to predict future growth (in 2009:Q1). The figure also includes the realized values of GDP growth (black vertical line). Blue density = model with single regressor (one-quarter-lagged GDP growth); red density = model with two regressors (one-quarter-lagged GDP growth and one-quarter-lagged FCI). Figure 3.7. Probability Densities of GDP Growth for the Depths of the Global Financial Crisis (Probability) 1. United States Accounting for financial conditions generates a more pessimistic outlook for risks to growth one quarter before 2009:Q1. 2. Chile 104 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Table 3.1. Forecast of GDP Growth Distribution for the Global Financial Crisis with and without Financial Conditions Indices (Cumulative probability of actual 2009:Q1 growth outturn, percent) Selected Advanced Economies Selected Emerging Market Economies Real-time FCI Augmented FCI Augmented Autoregressive Real-time FCI Augmented FCI Augmented Autoregressive Germany Brazil One quarter ahead for 2009:Q1 5.4 2.4 0.0 One quarter ahead for 2009:Q1 35.5 39.6 7.5 Four quarters ahead for 2009:Q1 0.1 0.4 0.0 Four quarters ahead for 2009:Q1 4.2 5.0 5.5 Sweden Chile One quarter ahead for 2009:Q1 6.5 5.9 4.8 One quarter ahead for 2009:Q1 6.4 8.0 2.6 Four quarters ahead for 2009:Q1 0.0 0.8 0.5 Four quarters ahead for 2009:Q1 4.0 1.7 2.0 United Kingdom South Africa One quarter ahead for 2009:Q1 29.8 29.5 5.8 One quarter ahead for 2009:Q1 7.2 4.6 0.8 Four quarters ahead for 2009:Q1 0.8 2.8 1.5 Four quarters ahead for 2009:Q1 5.3 6.2 1.6 United States Turkey One quarter ahead for 2009:Q1 46.7 30.3 8.5 One quarter ahead for 2009:Q1 31.5 27.1 5.3 Four quarters ahead for 2009:Q1 2.6 4.0 4.2 Four quarters ahead for 2009:Q1 3.5 2.3 2.8 Sources: Bloomberg Finance L.P.; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: The table depicts the cumulative probabilities of a growth outcome in 2009:Q1 of less than or equal to the actual growth outturn (quarter over quarter, annualized) in that period drawn from conditional density forecasts of GDP growth made four quarters earlier (that is, in 2008:Q1). The left column depicts probabilities from the model with financial conditions indices (FCIs) estimated with information available in real time. The middle column depicts probabilities from the model with FCIs estimated with full in-sample information. The right column depicts probabilities from the autoregressive model of GDP growth. Autoregressive = quantile regression of one-year-ahead GDP growth on current quarter GDP growth; FCI augmented = quantile regression of one-year-ahead GDP growth on current quarter GDP growth and FCI. Table 3.2. Market Consensus Forecasts for the Global Financial Crisis Were Considerably More Optimistic Than Forecasts Based on Financial Conditions Growth Forecasts Conditional on Lagged GDP and FCI Consensus Growth Forecasts Growth Outturn in 2009:Q112008:Q1 2008:Q4 2008:Q1 2008:Q4 Brazil 3.1 −4.3 4.6 2.1 −6.9 Canada 1.7 −5.3 1.7 −0.1 −8.8 France 1.9 −1.2 1.6 −0.6 −6.4 Mexico 2.6 −3.6 2.8 −0.1 −14.7 South Africa 2.7 −2.0 4.7 2.7 −6.1 Switzerland 1.9 −2.0 2.8 −1.6 −5.5 Turkey 3.4 −7.4 4.8 0.8 −15.2 United States 1.9 −3.8 1.6 −1.3 −5.4 Sources: Bloomberg Finance L.P.; Consensus Economics; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: Columns 2 and 3 of the table denote, respectively, the conditional mean forecasts for (quarter over quarter, annualized) GDP growth in 2009:Q1 made one quarter and one year earlier based on an ordinary least squares regression of future GDP growth on current quarter FCI and GDP growth. Columns 4 and 5 denote market consensus forecasts for 2009:Q1 made one quarter and four quarters earlier, respectively. Column 6 depicts the actual growth outturn. FCI = financial conditions index. 1Based on data available as of August 3, 2017. 105 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 This explains why autoregressive-conditional quantile forecasts were behind the curve, even at the end of 2008. A few quarters earlier, in early 2008, FCIs had risen from their boom-time lows but were only at their historical averages (for emerging market economies) or at levels corresponding to recessions significantly milder than the outturn of the first quarter of 2009 (for advanced economies). Consequently, one year ahead, conditioning on FCIs does not result in signifi- cantly different predictions of growth during the global financial crisis relative to either consensus forecasts or autoregressive-conditional quantile forecasts. Partitioning the FCI constituents into subindices enables the forecasts conditioned on financial indi- cators to regain relative predictive gains over longer time horizons in several countries (Table 3.3).18 One-year-ahead conditional forecasts for annual growth assign significantly higher likelihood to growth outcomes less than or equal to the outturn of the first quarter of 2009 when the forecasts are based on infor- mation in financial indicators than when based only on 18The contribution of each financial indicator to its group subin- dex is determined according to a methodology designed to improve forecast performance as discussed in Annex 3.2. lagged GDP growth. This is the likely consequence of separating credit aggregates from asset prices, thereby allowing their information to gain greater weight at horizons beyond one quarter. Real-time conditional density forecasts of economic growth are almost identical to those reported above for in-sample forecasts (Figures 3.8 and 3.9). Hence, using information in FCIs and in partitioned financial indicators available only up to one to four quarters earlier than the first quarter of 2009 would result in conditional likelihoods being assigned to the actual growth outcomes that are very similar to those obtained through in-sample forecasts using financial indicators (Tables 3.1 and 3.3).19 19This is implied by the fact that real-time forecasts of the quan- tiles of future GDP growth obtained through recursive estimation are almost identical to (or, below the median quantile, often lower than) those obtained through the in-sample forecasts. The fact that a majority of financial indicators are available only from the mid-1990s to the mid-2000s, especially for emerging market econ- omies, prevents backtesting of the model’s forecasting ability relative to earlier crisis-related recessions, for example, in Sweden (1990–92), Mexico (1994), east Asia (1997), and Turkey (2000–01), among others. More generally, low-frequency and limited time series data on real and financial variables preclude implementation with suffi- cient power of appropriate out-of-sample forecast evaluation tests described in Corradi and Swanson 2006 and Komunjer 2013. Table 3.3. Forecast of GDP Growth Distribution for the Global Financial Crisis: Comparing Partitioned and Univariate Financial Conditions Indices with Autoregressions (Cumulative probability of actual 2009:Q1 growth outturn, percent) Selected Advanced Economies Selected Emerging Market Economies Real-time Partitioned Financial Variables Partitioned Financial Variables FCI Augmented Autoregressive Real-time Partitioned Financial Variables Partitioned Financial Variables FCI Augmented Autoregressive Germany Brazil Four quarters ahead for 2009:Q1 0.8 0.7 0.4 0.0 Four quarters ahead for 2009:Q1 14.0 6.7 5.0 5.5 Sweden Chile Four quarters ahead for 2009:Q1 7.1 5.7 0.8 0.5 Four quarters ahead for 2009:Q1 12.7 10.4 1.7 2.0 United Kingdom South Africa Four quarters ahead for 2009:Q1 6.4 5.0 2.8 1.5 Four quarters ahead for 2009:Q1 5.4 7.3 6.2 1.6 United States Turkey Four quarters ahead for 2009:Q1 24.7 19.1 4.0 4.2 Four quarters ahead for 2009:Q1 7.4 4.4 2.3 2.8 Sources: Bloomberg Finance L.P.; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: The table depicts the cumulative probabilities of a growth outcome in 2009:Q1 of less than or equal to the actual growth outturn (quarter over quarter, annualized) in that period drawn from conditional density forecasts of GDP growth made four quarters earlier (that is, in 2008:Q1) according to the four alternative methodologies. Autoregressive = quantile regression of one-year-ahead GDP growth on current quarter GDP growth; FCI = financial conditions index; FCI augmented = quantile regression of one-year-ahead GDP growth on current quarter GDP growth and FCI; partitioned financial variables = quantile regression of one-year-ahead GDP growth on current quarter GDP growth and subindices of financial indicators. 106 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 –20 –10 0 10 20 19 73 :Q 1 75 :Q 2 77 :Q 3 79 :Q 4 82 :Q 1 84 :Q 2 86 :Q 3 88 :Q 4 91 :Q 1 93 :Q 2 95 :Q 3 97 :Q 4 20 00 :Q 1 02 :Q 2 04 :Q 3 06 :Q 4 09 :Q 1 11 :Q 2 13 :Q 3 15 :Q 4 –30 –15 0 15 30 19 91 :Q 1 92 :Q 2 93 :Q 3 94 :Q 4 96 :Q 1 97 :Q 2 98 :Q 3 99 :Q 4 20 01 :Q 1 02 :Q 2 03 :Q 3 04 :Q 4 06 :Q 1 07 :Q 2 08 :Q 3 09 :Q 4 11 :Q 1 12 :Q 2 13 :Q 3 14 :Q 4 16 :Q 1 –30 –20 –10 0 10 20 19 73 :Q 1 75 :Q 2 77 :Q 3 79 :Q 4 82 :Q 1 84 :Q 2 86 :Q 3 88 :Q 4 91 :Q 1 93 :Q 2 95 :Q 3 97 :Q 4 20 00 :Q 1 02 :Q 2 04 :Q 3 06 :Q 4 09 :Q 1 11 :Q 2 13 :Q 3 15 :Q 4 –20 –10 0 10 20 30 19 91 :Q 1 92 :Q 2 93 :Q 3 94 :Q 4 96 :Q 1 97 :Q 2 98 :Q 3 99 :Q 4 20 01 :Q 1 02 :Q 2 03 :Q 3 04 :Q 4 06 :Q 1 07 :Q 2 08 :Q 3 09 :Q 4 11 :Q 1 12 :Q 2 13 :Q 3 14 :Q 4 16 :Q 1 –15 –10 –5 0 5 10 15 19 81 :Q 1 83 :Q 3 86 :Q 1 88 :Q 3 91 :Q 1 93 :Q 3 96 :Q 1 98 :Q 3 20 01 :Q 1 03 :Q 3 06 :Q 1 08 :Q 3 11 :Q 1 13 :Q 3 16 :Q 1 –40 –20 0 20 40 19 91 :Q 1 92 :Q 2 93 :Q 3 94 :Q 4 96 :Q 1 97 :Q 2 98 :Q 3 99 :Q 4 20 01 :Q 1 02 :Q 2 03 :Q 3 04 :Q 4 06 :Q 1 07 :Q 2 08 :Q 3 09 :Q 4 11 :Q 1 12 :Q 2 13 :Q 3 14 :Q 4 16 :Q 1 –20 –15 –10 –5 0 5 10 15 19 73 :Q 1 75 :Q 2 77 :Q 3 79 :Q 4 82 :Q 1 84 :Q 2 86 :Q 3 88 :Q 4 91 :Q 1 93 :Q 2 95 :Q 3 97 :Q 4 20 00 :Q 1 02 :Q 2 04 :Q 3 06 :Q 4 09 :Q 1 11 :Q 2 13 :Q 3 15 :Q 4 –10 –5 0 5 10 19 91 :Q 1 92 :Q 2 93 :Q 3 94 :Q 4 96 :Q 1 97 :Q 2 98 :Q 3 99 :Q 4 20 01 :Q 1 02 :Q 2 03 :Q 3 04 :Q 4 06 :Q 1 07 :Q 2 08 :Q 3 09 :Q 4 11 :Q 1 12 :Q 2 13 :Q 3 14 :Q 4 16 :Q 1 Sources: Bloomberg Finance L.P.; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: This figure shows the estimates of the 5th (bottom), 50th (middle), and 95th (top) quantiles of GDP growth based on the quantile regression model where one-quarter-ahead GDP growth is regressed on current date financial conditions index and GDP growth. In-sample estimation Real-time estimation Figure 3.8. In-Sample and Recursive Out-of-Sample Quantile Forecasts: One Quarter Ahead (Percent) 1. Germany 2. Brazil 3. United Kingdom 4. Chile 7. United States 8. South Africa 5. Sweden 6. Turkey 107 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 –6 –4 –2 0 2 4 6 8 2001 02 03 04 05 06 07 08 09 10 11 12 13 14 15 –10 –5 0 5 10 15 2001 02 03 04 05 06 07 08 09 10 11 12 13 14 15 –4 –2 0 2 4 6 2001 02 03 04 05 06 07 08 09 10 11 12 13 14 15 –5 0 5 10 15 2001 02 03 04 05 06 07 08 09 10 11 12 13 14 15 –10 –5 0 5 10 2001 02 03 04 05 06 07 08 09 10 11 12 13 14 15 –10 –5 0 5 10 15 20 2001 02 03 04 05 06 07 08 09 10 11 12 13 14 15 –8 –6 –4 –2 0 2 4 6 2001 02 03 04 05 06 07 08 09 10 11 12 13 14 15 –4 –2 0 2 4 6 8 2001 02 03 04 05 06 07 08 09 10 11 12 13 14 15 In-sample estimation Real-time estimation Sources: Bloomberg Finance L.P.; Haver Analytics; IMF, Global Data Source and World Economic Outlook databases; Thomson Reuters Datastream; and IMF staff estimates. Note: This figure shows the estimates of the 25th (bottom), 50th (middle), and 75th (top) quantiles of GDP growth based on the quantile regression model with partitioned financial indicators replacing the univariate financial conditions index. Figure 3.9. In-Sample and Recursive Out-of-Sample Quantile Forecasts: Four Quarters Ahead (Percent) 1. Germany 2. Brazil 3. United Kingdom 4. Chile 7. United States 8. South Africa 5. Sweden 6. Turkey 108 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 This augurs well for the parameter stability of the chapter’s forecast model, demonstrating that its fore- casts and relative predictive ability are not an artifact of incorporating events such as the global financial crisis into estimates of its parameters. Policy Implications The chapter’s findings underscore the importance of policymakers maintaining heightened vigilance regarding risks to growth during periods of benign financial condi- tions that may provide a fertile breeding ground for the accumulation of financial vulnerabilities. Changes in the domestic price of risk appear to be potent signals of immi- nent threats to growth and can be useful for swift deploy- ment of monetary easing and crisis-management policy actions. Incorporating information in slower-moving indi- cators could help better calibrate countercyclical policies, even though doing so systematically would require com- bining the information derived from the models described in this chapter with appropriate structural models. This chapter develops a new macroeconomic measure of financial stability by linking financial conditions to the probability distribution of future GDP growth. Since policymakers care about the whole distribution of future GDP growth, linking the state of the financial system to such a distribution would enhance macro-financial surveillance. Policymakers would be able to specify bad outcomes in terms of their risk preference or tolerance and undertake appro- priate action based on the information provided by financial conditions. Thus, the new modeling approach can be a powerful tool for forecasting and policy development. Financial conditions contain useful information with which to help forecast risks to economic growth at short- and medium-term horizons. Thus, the tools used and developed in this chapter can help policy- makers assess the risks to the real economy associ- ated with various states of the financial system. For example, at the current juncture, elevated leverage signals downside risks to growth in the medium term, although in the short term, this risk is mitigated by the low price of risk. However, a scenario of rapid decom- pression in spreads and an increase in financial market volatility would add to the risks arising from leverage, significantly worsening the growth outlook. Policymakers could use the information provided by such a surveillance framework to identify immi- nent threats and take swift countervailing action over very short horizons. If a rapid increase in the price of risk at a time of elevated leverage or balance sheet mismatches indicates an imminent threat to the economy, policymakers can quickly ease monetary policy and deploy a wide range of crisis-management and -prevention measures to prevent tail events or reduce their magnitude. During the global financial crisis, bilateral and multilateral swap lines, general creditor guarantees, asset purchase programs, and emergency liquidity facilities, among others, were marshalled by a number of countries at relatively short notice. The framework developed in this chapter could potentially help policymakers design policy actions to respond in a timely manner to threats to financial stability indicated by changes in financial conditions. It is natural to think of calibrating policy actions on the state of financial conditions—much as monetary policy action is calibrated to information on inflation and output under standard Taylor rules. For example, countercyclical macroprudential tools, such as bank capital buffers and limits on loan-to-value ratios, could be designed and calibrated to contain the growth of financial vulnerabilities in the presence of loose finan- cial conditions. In this regard, the estimated forecast relationships from the GDP growth-at-risk model of this chapter can also be used to calibrate structural models that are amenable to counterfactual analysis and policy development.20 Practical implementation of forecasting of risks to growth based on financial conditions will require data gaps to be closed. This need strengthens the case for greater data-gathering efforts. It also points to a need for continuous calibration of these types of models as data gaps gradually close and for incorporation of country-level information that may substitute for the lack of standard financial indicators. In this way, policymakers and others could significantly improve on the forecasting power of the models presented here by incorporating rich country-level information to com- plement the models’ broad financial indicators. As local financial markets undergo structural developments, and authorities consider certain financial indicators to 20One option could be to use the conditional density forecasts of GDP growth to calibrate the higher moments (for example, conditional volatility or skewness) of structural models that embed financial accelerator mechanisms such as the one described in Annex 3.1. 109 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 be increasingly relevant, these could also be gradually incorporated into the analysis.21 Annex 3.1. Financial Vulnerabilities and Growth Hysteresis in Structural Models22 An Illustrative Simulation A simulation exercise of a structural model is con- ducted to illustrate the nonlinear response of output growth to shocks depending on the level of financial vulnerabilities. The exercise shows that embedding an occasionally binding funding constraint on borrowers in an otherwise standard New Keynesian (NK) open economy structural model is sufficient to generate two key stylized facts. These are, first, that the steady-state probability distribution of GDP growth is negatively skewed and, second, that asset prices and credit aggre- gates are leading indicators of risks to GDP growth. In the presence of financial frictions, the response of output growth to shocks is highly nonlinear. Recent advances in macroeconomic theory have clarified the importance of financing constraints on borrowers and intermediaries in generating this response. In their seminal contributions, Bernanke and Gertler (1989); Kiyotaki and Moore (1997); and Bernanke, Gertler, and Gilchrist (1999) clarified the role of credit market frictions in determining fluctuations in real economic activity. Their linear real business cycle models embed a financial accelerator mechanism in which endogenous developments in credit markets propagate and amplify shocks to the real economy. Although these models explain how financial frictions increase the amplitude of real business cycles, they do not shed light on how and when they can increase the duration of those cycles or generate extreme, unlikely negative outcomes (asymmetry, or tail risk). The key insight of recent advances in business cycle theory is that this outcome depends on individual financial decisions of banks, firms, and households that fail to take into consider- ation dynamic credit supply externalities implied by their decisions. That is, individual borrowers fail to 21The methodology developed in this chapter is used to model the impact of financial vulnerabilities on GDP growth. It is flexible in the inputs it can receive. In countries where risks to the real economy posed by amplifiers, whether real or fiscal, are not traded in deep financial markets, corresponding nonfinancial indicators could also be used as inputs. 22Prepared by Mitsuru Katagiri. (This annex is a summary of Katagiri, forthcoming.) take into account the fact that once aggregate leverage is sufficiently high, shocks can activate occasionally binding collateral constraints (OBCCs). This, in turn, can generate a vicious cycle of deleveraging and nega- tive asset price spirals that clog credit intermediation, consumption, investment, and growth.23 The simulation exercise embeds an OBCC into an NK open economy dynamic general equilibrium model. The OBCC is modeled as in Kiyotaki and Moore 1997. To tease out implications for optimal policy, nominal frictions based on an open economy NK model are incorporated in the spirit of Galí and Monacelli 2005. The main features of the model are as follows: Households are endowed with trad- able goods as in Bianchi 2011, while they produce nontradables using capital and labor. Households maximize their lifetime utility by choosing an inter- temporal portfolio of tradable and nontradable goods for consumption and supplying labor to the produc- tion process. Their borrowing must be lower than a fixed fraction of their capital value (that is, there is a collateral constraint). The nontradables sector is monopolistically competitive, and price setting is sub- ject to nominal frictions. Asset prices are determined under a fixed supply of capital. Nominal interest rates are set under a standard Taylor rule responding to inflation and output. The exchange rate is pinned down by the uncovered interest parity condition. The parameters are calibrated based on standard values in the literature of an OBCC model and an open econ- omy NK model, including Bianchi 2011 and Galí and Monacelli 2005. The simulated density of future output is shown to be negatively skewed; that is, it has a fat left tail, indicating a greater risk of severe recession. The unconditional distribution of future output (Annex Figure 3.1.1, panel 3) is negatively skewed—the skew- ness measure, at –1.51, is statistically significant. In the simulation, as in reality, the collateral constraint does not typically bind. Thus, the evolution of all economic variables, including output, is standard for the most part. However, when the OBCC binds (a rare event), output and asset prices decline significantly because 23For models embedding OBCCs on end-borrowers, see Bianchi 2011; Korinek and Simsek 2016; and Bianchi and Mendoza, forthcoming. For OBCCs or value-at-risk constraints on interme- diaries, see He and Krishnamurthy 2013 and Brunnermeier and Sannikov 2014. 110 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 of the vicious cycle of asset fire sales and tighter credit conditions, and output suffers. The simulation exercise clearly indicates the utility of conditioning the growth outlook on asset prices. Risk premiums in the simulation exercise are defined as the return on capital minus the inverse of the stochastic discount factor, as is standard.24 Annex Figure 3.1.1 shows the conditional density of output in period t, given that the risk premium in period t − 1 is less than 30 basis points (the case of high asset prices depicted in panel 1) and more than 30 basis points (the case of low asset prices depicted in panel 2). Those two panels indicate that when risk premiums rise (equivalently, when asset prices fall), the conditional density of one-period-ahead output shifts to the left and becomes negatively skewed. Higher risk premiums predict a lower average value of one-period-ahead output and a more pessimistic risk outlook (fatter left tail). Asset prices and credit aggregates can also be useful leading indicators of recessions or financial crises. The relationship between one-period-ahead output and risk premiums (Annex Figure 3.1.2, panel 1) indicates that the lower quantile of output declines significantly with rising risk premiums, whereas its upper quantile is significantly less sensitive. The relationship between one-period-ahead output and the credit-to-GDP ratio shows that a financial crisis occurs only when the ratio is at a historically high level (Annex Figure 3.1.2, panel 2). Finally, risk premiums and credit-to-output ratios are significantly higher than their steady-state values for several peri- ods before a crisis (Annex Figure 3.1.3). Calibrating Policy Rules to Attenuate Risks to Growth from Financial Vulnerability Macroprudential policy contingent on the state of financial conditions can mitigate the adverse real effects of financial crises. The decentralized equilibrium described in the previous section of this annex is not socially optimal because agents fail to take into consid- eration the negative systemic externalities of their lever- age choices on asset prices. Borrowers’ resulting excess leverage increases the frequency of financial crises. 24Note that risk premiums based on this definition are not directly observable in the data, but are conceptually close to the excess return of risk assets as defined in Gilchrist and Zakrajšek 2012 and hence can be calculated from financial market data. 0 2 4 6 0.94 0.95 0.96 0.97 0.98 0.99 1.01 1.02 1.03 1.04 1.05 One-period-ahead output (Normalized; steady state = 1.0) 0.94 0.95 0.96 0.97 0.98 0.99 1.01 1.02 1.03 1.04 1.05 One-period-ahead output (Normalized; steady state = 1.0) 0.94 0.95 0.96 0.97 0.98 0.99 1.01 1.02 1.03 1.04 1.05 One-period-ahead output (Normalized; steady state = 1.0) 0 1 2 3 0 2 4 6 8 × 104 × 104 × 104 Source: IMF staff estimates. Annex Figure 3.1.1. Conditional Densities of Growth with High and Low Asset Prices—One-Period-Ahead Forecasts (Frequency) 1. High Asset Prices 2. Low Asset Prices 3. Unconditional 111 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 Bianchi (2011) and Bianchi and Mendoza (forthcom- ing) show that a macroprudential tax (that is, a tax on debt before the crisis) that is contingent on the state of financial conditions can prevent excess leverage and implement the socially optimal outcome as a decen- tralized equilibrium. This socially optimal outcome can also be implemented by a regulation on loan-to-value (LTV) ratios. Once the optimal state-contingent macroprudential policy (taxes on debt or LTV regulation) is intro- duced, vulnerability to a recession (as measured by the negative skewness of the output distribution) is significantly mitigated. In the baseline simulation of the equilibrium without optimal macroprudential policy, 0.90 0.94 0.98 1.02 1.06 0 1 32 O ne -p er io d- ah ea d ou tp ut O ne -p er io d- ah ea d ou tp ut Risk premium (percent) 0.88 0.92 0.96 1.00 1.04 1.08 0.08 0.10 0.12 0.14 Credit-to-output ratio Source: IMF staff estimates. Annex Figure 3.1.2. One-Period-Ahead GDP and Financial Conditions (Normalized; steady state = 1.0) 1. Risk Premium Increasing risk premiums signal a more pessimistic growth outlook ... 2. Credit-to-Output Ratio ... as does elevated leverage. 0.94 0.95 0.96 0.97 0.98 0.99 1.00 0.85 0.90 0.95 1.00 1.05 1.10 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 t – 4 t – 3 t – 2 t – 1 t t + 1 t + 2 t + 3 t + 4 t – 4 t – 3 t – 2 t – 1 t t + 1 t + 2 t + 3 t + 4 t – 4 t – 3 t – 2 t – 1 t t + 1 t + 2 t + 3 t + 4 Source: IMF staff estimates. Note: The crisis happens in period 5 (t ) in the figures. The crisis is defined as a period in which output declines by more than 3 percent. The red dashed lines denote steady-state values. Annex Figure 3.1.3. Asset Prices and Credit Aggregates before and after a Financial Crisis 1. Output (Normalized; steady state = 1.0) Severe economic contractions are preceded by several periods of excessive leverage and, shortly before the crisis, by sharply rising risk premiums. 2. Credit-to-Output Ratio (Normalized; steady state = 1.0) 3. Risk Premium (Percent) 112 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 the probability of a recession driven by a financial crisis is 1.3 percent, and the skewness of the density of future GDP growth at –1.51 is statistically significant. Implementation of the state-contingent debt tax or state-contingent LTV regulation reduces these values to, respectively, 0.5 percent and –0.66. A simple policy rule conditioned on financial indicators comes close to implementing the optimal macroprudential policy. The optimal policy itself is a complex nonlinear function of state variables and is probably too complicated to implement in practice.25 Fortuitously, a simple rules-based macroprudential policy responding to vulnerability measures does a good job of mitigating the harmful effects of finan- cial crises. Risk premiums are used to improve the 25The nonlinearity stems from the fact that policymakers should raise borrowing costs through taxes or LTV regulations only when a crisis is predicted. performance of a simple rules-based macroprudential policy because they have predictive power for the crisis. Annex Figure 3.1.4 compares the evolution of real and financial indicators under a simple policy rule whereby debt taxes are a linear function of risk premiums to the baseline equilibrium. Policy based on a simple linear rule delivers almost the same performance as the optimal policy, implying that financial conditions such as risk premiums are useful for conducting macropru- dential policies in practice.26 26There are two caveats. First, all crises in the OBCC model are caused by a simple collateral constraint, whereas many other factors can contribute to financial crises. Second, the model assumes that policymakers can immediately respond to vulnerabilities. If there is a delay in policy reactions or their transmission to the real economy, the policy implications may be different. 0.94 0.95 0.96 0.97 0.98 0.99 1.00 t – 4 t – 3 t – 2 t – 1 t t + 1 t + 2 t + 3 t + 4 t – 4 t – 3 t – 2 t – 1 t t + 1 t + 2 t + 3 t + 4 t – 4 t – 3 t – 2 t – 1 t t + 1 t + 2 t + 3 t + 4 t – 4 t – 3 t – 2 t – 1 t t + 1 t + 2 t + 3 t + 4 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 0.85 0.90 0.95 1.00 1.05 1.10 1.4 1.6 1.8 2.0 2.2 Baseline Simple Source: IMF staff estimates. Annex Figure 3.1.4. Simple Debt Tax Ameliorates Risk of Leverage-Induced Recessions 1. Output (Normalized; steady state = 1.0) 2. Asset Prices (Normalized; steady state = 1.0) 3. Credit-to-Output Ratio (Normalized; steady state = 1.0) 4. Inflation (Percent) 113 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 Annex 3.2. Estimating Financial Conditions Indices27 Univariate Financial Conditions Indices A simple way to build a summary measure of financial conditions is to construct univariate financial conditions indices (FCIs) following the approach in the April 2017 GFSR, although with some important modifications. The main change is that the coverage of financial indicators is expanded to include additional information relevant to assessing domestic financial vulnerabilities. FCIs will therefore also include variables that summarize global risk sen- timent (Chicago Board Options Exchange Volatility Index [VIX], Merrill Lynch Option Volatility Esti- mate [MOVE] Index), credit aggregates that directly indicate the level of financial vulnerability in the economy, and commodity prices and exchange rates that may influence and reflect the ease of funding and financial constraints—for example, by altering borrowers’ net worth.28 Following the methodology presented in Annex 3.1 of the April 2017 GFSR, FCIs are reestimated for 11 advanced economies starting in 1973 and for 10 emerging market economies starting in 1991. A set of 19 financial indicators is used to capture both domestic and global developments influencing a coun- try’s financial conditions (see Annex Table 3.2.1 for country coverage and Annex Table 3.2.2 for variables included and data sources). The FCIs are estimated based on Koop and Korobilis 2014 and build on the estimation of the time-varying parameter vector autore- gression model of Primiceri (2005) and dynamic factor 27Prepared by Romain Lafarguette and Dulani Seneviratne. 28An important reason to expand coverage to aggregates is that beyond a few advanced economies, it is unlikely that developments in asset prices provide an adequately encompassing and timely sum- mary of the information regarding vulnerabilities that is contained in these financial aggregates. Thus, conditioning directly on the information content of the aggregates may improve the accuracy of forecasts of the risk outlook for growth. models of Doz, Giannone, and Reichlin (2011).29 This approach has two advantages. First, it can control for current macroeconomic conditions. Second, it allows for dynamic interaction between the FCIs and macro- economic conditions, which can also evolve over time. The model takes the following form: x t = λ t y Y t + λ t f f t + u t , [ Y t f t ] = B 1,t [ Y t – 1 f t – 1 ] + B 2,t [ Y t – 2 f t – 2 ] + . . . + ε t , (A3.2.1) in which x is a vector of financial indicators, Y is a vector of macroeconomic variables of interest (includ- ing real GDP growth and inflation), λ t y are regression coefficients, λ t f are the factor loadings, and f t is the latent factor, interpreted as the FCI. Univariate FCIs offer a parsimonious way of sum- marizing the information in several financial indica- tors, which could be advantageous from a forecasting perspective because it can help reduce parameter uncertainty. However, the weight of each variable is not necessarily driven by economic considerations of relative importance as suggested either by theory or by country-specific characteristics. For example, movements in asset prices may be effective in pin- pointing risks at short horizons, but slower-moving credit aggregates are likelier to yield more infor- mation at longer time horizons. Moreover, while asset prices are likely to be an adequate summary of financial vulnerabilities in some advanced economies, credit aggregates may possess significantly greater information content in emerging market economies. Consequently, financial indicators need not receive the same weight across different time horizons and countries; therefore, as described in the second sec- tion of this annex, the chapter also uses an approach that seeks to exploit the information content of 29The FCIs are estimated using Koop and Korobilis’ (2014) code (https:// sites .google .com/ site/ dimitriskorobilis/ matlab). Annex Table 3.2.1. Country Coverage Australia Germany Mexico Turkey Brazil India Russia United Kingdom Canada Indonesia South Africa United States Chile Italy Spain China Japan Sweden France Korea Switzerland Source: IMF staff. 114 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 Annex Table 3.2.2. Data Sources Variables Description Source Domestic-Level Variables Term Spreads Yield on 10-year government bonds minus yield on three-month Treasury bills Bloomberg Finance L.P.; IMF staff Interbank Spreads Interbank interest rate minus yield on three-month Treasury bills Bloomberg Finance L.P.; IMF staff Change in Long-Term Real Interest Rate Percentage point change in the 10-year government bond yield, adjusted for inflation Bloomberg Finance L.P.; IMF staff Corporate Spreads Corporate yield of the country minus yield of the benchmark country; JPMorgan CEMBI Broad is used for emerging market economies where available Bloomberg Finance L.P.; Thomson Reuters Datastream Equity Returns (local currency) Log difference of the equity indices Bloomberg Finance L.P. House Price Returns Log difference of the house price index Bank for International Settlements; Haver Analytics; IMF staff Equity Return Volatility Exponential weighted moving average of equity price returns Bloomberg Finance L.P.; IMF staff Change in Financial Sector Share Log difference of the market capitalization of the financial sector to total market capitalization Bloomberg Finance L.P. Credit Growth Percent change in the depository corporations’ claims on private sector Bank for International Settlements; Haver Analytics; IMF, International Financial Statistics database Sovereign Spreads Yield on 10-year government bonds minus the benchmark country’s yield on 10-year government bonds Bloomberg Finance L.P.; IMF staff Banking Sector Vulnerability Expected default frequency of the banking sector Moody’s Analytics, CreditEdge; IMF staff Exchange Rate Movements Change in US dollar per national currency exchange rate; for the United States, Bloomberg Finance L.P.’s DXY index is used Bloomberg Finance L.P.; IMF, Global Data Sources and International Financial Statistics databases Domestic Commodity Price Inflation A country-specific commodity export price index constructed following Gruss 2014, which combines international commodity prices and country-level data on exports and imports for individual commodities; change in the estimated country-specific commodity export price index is used Bloomberg Finance L.P.; IMF, Global Data Sources database; United Nations, COMTRADE database; IMF staff Trading Volume (equities) Equity markets’ trading volume, calculated as level to 12-month moving average Bloomberg Finance L.P. Market Capitalization (equities) Market capitalization of the equity markets, calculated as level to 12-month moving average Bloomberg Finance L.P.; Thomson Reuters Datastream Market Capitalization (bonds) Bonds outstanding, calculated as level to 12-month moving average Dealogic; IMF staff Change in Credit to GDP Change in credit provided by domestic banks, all other sectors of the economy, and nonresidents (in percent of GDP) Bank for International Settlements; Haver Analytics; IMF staff Real GDP Growth Percent change in GDP at constant prices IMF, World Economic Outlook database Inflation Percent change in the consumer price index Haver Analytics; IMF, International Financial Statistics database Global-Level Variables VIX Chicago Board Options Exchange Market Volatility Index Bloomberg Finance L.P.; Haver Analytics MOVE Merrill Lynch Option Volatility Estimate Index Bloomberg Finance L.P. Source: IMF staff. Note: CEMBI = Corporate Emerging Markets Bond Index; DXY = Dollar Index Spot; MOVE = Merrill Lynch Option Volatility Estimate Index; VIX = Chicago Board Options Exchange Volatility Index. 115 C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k International Monetary Fund | October 2017 financial indicators in a manner that is more sensitive to countries and time horizons. Data Partitioning Based on Linear Discriminant Analysis The individual financial indicators are aggregated into groups using linear discriminant analysis (LDA), a data-reduction technique (Annex Table 3.2.3). LDA aims to project a data set onto a lower-dimensional space while ensuring adequate separation of data into categories. LDA is similar to principal components analysis (PCA) in the sense that it maximizes the common variance among a set of variables, but it diverges from PCA by also ensuring that the linear combination of the variables discriminates across the classes of another categorical variable of interest. In the framework of the chapter, this categorical variable is a dummy variable, defined at the country level, equal to one when future GDP growth at a one-year horizon is below the 20th percentile of historical outcomes and equal to zero otherwise. Consequently, the loading on each individual financial indicator in the LDA is determined in a way that maximizes its contribu- tion to discriminating between periods of low GDP growth and periods of normal GDP growth. This is convenient from the chapter’s perspective because it allows for a link between financial indicators and GDP growth in the data-reduction process. By contrast, the PCA approach aggregates only information about the common trend among financial indicators.30 30LDA assumes independence of normally distributed data and homoscedastic variance among each class, although LDA is consid- ered robust when these assumptions are violated. See Duda, Hart, and Stork 2001. See Izenman 2013 for a thorough exposition of the LDA technique. Annex 3.3. The Conditional Density of Future GDP Growth31 Quantile Regressions The estimation of the conditional density forecast is conducted through quantile projections.32 This approach starts by using quantile regressions to directly estimate the conditional quantiles (q) of the forecast distribution of GDP growth ( y ) h quarters ahead, as a function of both its current level and current financial conditions (FC ): y t + h,q = β f,q h FC t + β y,q h y t + ϵ t,q h . (A3.3.1) In the baseline approach, FC corresponds to a pre- determined univariate financial conditions index (FCI) constructed in the manner described in Annex 3.2. The empirical model is subsequently modified to investigate the relative significance of asset prices, credit aggregates, and global or foreign factors in signaling risks to GDP growth in the near to medium term: y t + h,q = α p,q h p t + β a,q h Agg t + γ y,q h y t + ϕ f,q h f t + ϵ t,q h , (A3.3.2) in which p, Agg, and f correspond to the principal com- ponents of the price of risk (asset prices and risk spreads), 31Prepared by Sheheryar Malik and Romain Lafarguette. 32For an introduction to quantile regression, see Koenker 2005. As highlighted by Komunjer (2013), quantile regressions rely on specific functional form assumptions and have some important advantages in forecasting the conditional distribution of the variable of interest. These include the desirability of the conditional quantile estimator as a predictor of the true future quantile; robustness of the estimation to extreme outliers and violations of normality and homoscedasticity of the errors; flexibility, allowing for time-varying structural parame- ters and the optimal weighting of predictors depending on country, horizon, and the relevant portion of the distribution; and the ability to avoid overfitting (compared with more complex models such as copulas and extreme value theory). Annex Table 3.2.3. Partitioning of Financial Indicators into Groups Price of Risk Leverage Foreign Shocks Persistence Financial and Real Indicators (when available) Term spread Credit to GDP Bilateral exchange rate (US dollar to local currency) GDP growth Corporate spread Credit growth (quarterly) Short-term rate Commodity prices Real long-term rate VIX1 Sovereign spread Interbank spread Equity returns Equity historical volatility House price returns Source: IMF staff. 1 Except for the United States, for which VIX enters as a price-of-risk variable. VIX = Chicago Board Options Exchange Volatility Index. 116 G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ? International Monetary Fund | October 2017 credit aggregates, and global or foreign variables (com- modity prices, exchange rates, and global risk sentiment). This approach disentangles the contribution of changes in the price of risk from evolving credit aggregates and shocks to the external environment when it comes to forecasting risks to GDP growth. It thereby provides insight into which variables signal growth tail risks over various time horizons. This can help policymakers and others design a surveillance framework that seeks to embed information flowing in at different frequencies. Deriving the Density Forecast The quantile regression in equation (A3.3.1) delivers an estimate for the conditional quantile function (or inverse cumulative distribution function) h quarters ahead—that is, y ˆ t + h,q ( = β̂ f,q h FC t + β̂ y,q h y t ) . Given the noisiness of such estimates in practice, recovering the corresponding predictive probability density function will inevitably require smoothing of the quantile func- tion. In this chapter, this is accomplished via fitting a parametric form skewed t distribution.33 For each quarter, the analysis attempts to pin down four parameters of the predictive den- sity {​μ​ t + h , s t + h , v t + h , ξ t + h } by minimizing the squared distance between the estimated quantile function, y ˆ t + h,q , and (theoretical) quantile func- tion y q f ( ​μ​ t + h , s t + h , v t + h , ξ t + h ) corresponding to the above skewed t distribution (see Giot and Laurent 2003). The four parameters (​μ, s, v, ξ ) are, respectively, the location, scale, degrees of freedom, and the shape of skewed t distribution. Specifically, the 5th, 25th, 50th, 75th, and 95th percentiles are matched via {​μ​ t + h , s t + h , v t + h , ξ t + h } = μ t + h , s t + h , v t + h , ξ t + h argmin ∑ q { y ˆ t + h,q − y q f ( μ t + h , s t + h , v t + h , ξ t + h ) } 2 , in which μ t + h ∈ ℝ , s t + h > 0 , v t + h ≥ 2, and
ξ t + h > 0 . Notwithstanding the skewness property,
33There are many choices for fitting a conditional density on the
set of conditional quantiles. Adrian, Boyarchenko, and Giannone
(2016) adopt a parametric approach focusing on a distribution
family chosen a priori (t skewed), whereas De Nicolò and Lucchetta
(2017) use a nonparametric approach. The functional form for the
skewed t distribution is motivated by Fernandez and Steel (1998)
and further explored and refined in Giot and Laurent 2003 and
Lambert and Laurent 2002; see also Boudt, Peterson, and Croux
2008. Alternative specifications for the skewed t distribution are
present in literature—for example, as put forth by Hansen (1994)
and Azzalini and Capitanio (2003). These are essentially equivalent
given a nonlinear transformation of the skewness parameter.
choice of a skewed t functional form is advantageous
from the perspective of flexibility. For example,
v → ∞, f ( y; μ, s, v, ξ) is characterized by tail proper-
ties resembling a Gaussian distribution. Moreover, the
density is symmetric for ξ = 1 .
References
Adrian, Tobias, Nina Boyarchenko, and Domenico Giannone.
2016. “Vulnerable Growth.” Federal Reserve Bank of New
York Staff Report 794.
Adrian, Tobias, and Nellie Liang. 2016. “Monetary Policy,
Financial Conditions, and Financial Stability.” Federal Reserve
Bank of New York Staff Report 690.
Adrian, Tobias, Emanuel Moench, and Hyun-Song Shin. 2010.
“Macro Risk Premium and Intermediary Balance Sheet
Quantities.” IMF Economic Review 58 (1): 179–207.
Adrian, Tobias, and Hyun-Song Shin. 2014. “Procyclical
Leverage and Value-at-Risk.” Review of Financial Studies 27
(2): 373–403.
Aguiar, Mark, and Gita Gopinath. 2007. “Emerging Market
Business Cycles: The Cycle Is the Trend.” Journal of Political
Economy 115 (1): 69–102.
Ang, Andrew, Monika Piazzesi, and Min Wei. 2006. “What
Does the Yield Curve Tell Us about GDP Growth?” Journal
of Econometrics 131 (1–2): 359–403.
Azzalini, Adelchi, and Antonella Capitanio. 2003. “Distributions
Generated by Perturbations of Symmetry with Emphasis on a
Multivariate Skew t Distribution.” Journal of the Royal Statisti-
cal Society: Series B 65: 367–89.
Bernanke, Ben S. 1983. “Nonmonetary Effects of the Financial
Crisis in the Propagation of the Great Depression.” American
Economic Review 73 (3): 257–76.
———, and Alan S. Blinder. 1992. “The Federal Funds Rate
and the Channels of Monetary Policy Transmission.” Ameri-
can Economic Review 82 (4): 901–21.
Bernanke, Ben S., and Mark Gertler. 1989. “Agency Costs,
Net Worth, and Business Fluctuations.” American Economic
Review 79 (1): 14–31.
———, and Simon Gilchrist. 1999. “The Financial Accelerator
in a Quantitative Business Cycle Framework.” In Handbook
of Macroeconomics, edited by J. B. Taylor and M. Woodford.
Amsterdam: Elsevier.
Bianchi, Javier. 2011. “Overborrowing and Systemic External-
ities in the Business Cycle.” American Economic Review 101
(7): 3400–26.
———, and Enrique Mendoza. Forthcoming. “Optimal
Time-Consistent Macroprudential Policy.” Journal of Polit-
ical Economy.
Boudt, Kris, Brian G. Peterson, and Christophe Croux. 2008.
“Estimation and Decomposition of Downside Risk for
Portfolios with Non-Normal Returns.” Journal of Risk 11
(2): 79–103.

117
C H A P T E R 3 F I N A N C I A L C O N d I T I O N S A N d G R O w T h A T R I S k
International Monetary Fund | October 2017
Brunnermeier, Markus K., and Lasse Heje Pedersen. 2009.
“Market Liquidity and Funding Liquidity.” Review of Finan-
cial Studies 22 (6): 2201–38.
Brunnermeier, Markus K., and Yuliy Sannikov. 2014. “A Macro-
economic Model with a Financial Sector.” American Economic
Review 104 (2): 379–421.
Caballero, Ricardo J., and Arvind Krishnamurthy. 2006. “Bub-
bles and Capital Flow Volatility: Causes and Risk Manage-
ment.” Journal of Monetary Economics 53 (1): 35–53.
Campbell, John Y. 1999. “Asset Prices, Consumption and the
Business Cycle.” In Handbook of Macroeconomics, edited by
John B. Taylor and Michael Woodford. Amsterdam: Elsevier.
———, Martin Lettau, Burton G. Malkiel, and Yexaio Xu.
2001. “Have Individual Stocks Become More Volatile? An
Empirical Exploration of Idiosyncratic Risk.” Journal of
Finance 56 (1): 1–43.
Christiano, Lawrence J., Martin Eichenbaum, and Charles L.
Evans. 2005. “Nominal Rigidities and the Dynamic Effects
of a Shock to Monetary Policy.” Journal of Political Economy
113 (1): 1–45.
Claessens, Stijn, Ayhan Kose, and Marco Terrones. 2011a.
“Financial Cycles: What? When? How?” CEPR Discussion
Paper 8379, Centre for Economic Policy Research, London.
———. 2011b. “How Do Business and Financial Cycles Inter-
act?” CEPR Discussion Paper 8396, Centre for Economic
Policy Research, London.
Corradi, Valentina, and Norman Swanson. 2006. “Predictive
Density Evaluation.” In The Handbook of Economic Forecast-
ing, edited by Graham Elliott, Clive W. J. Granger, and Allan
Timmermann. Amsterdam: Elsevier.
De Nicolò, Gianni, and Marcella Lucchetta. 2017. “Forecasting
Tail Risks.” Journal of Applied Econometrics 32 (1): 159–70.
Del Negro, Marco, and Frank Schorfheide. 2009. “Monetary
Policy Analysis with Potentially Misspecified Models.” Ameri-
can Economic Review 99 (4): 1415–50.
Doz, Catherine, Domenico Giannone, and Lucrezia Reich-
lin. 2011. “A Two-Step Estimator for Large Approximate
Dynamic Factor Models Based on Kalman Filtering.” Journal
of Econometrics 164 (1): 188–205.
Duda, Richard O., Peter E. Hart, and David G. Stork. 2001.
Pattern Classification. New York: Wiley-Interscience.
Estrella, Arturo, and Gikas A. Hardouvelis. 1991. “The Term
Structure as a Predictor of Real Economic Activity.” Journal of
Finance 46 (2): 555–76.
Estrella, Arturo, and Frederick S. Mishkin. 1998. “Predicting
U.S. Recessions: Financial Variables as Leading Indicators.”
Review of Economics and Statistics 80 (1): 45–61.
Fernandez, Carmen, and Mark F. J. Steel. 1998. “On Bayesian
Modelling of Fat Tails and Skewness.” Journal of the American
Statistical Association 93 (441): 359–71.
Friedman, Benjamin M., and Kenneth N. Kuttner. 1998. “Indi-
cator Properties of the Paper-Bill Spread: Lessons from Recent
Experience.” Review of Economics and Statistics 80 (1): 34–44.
Galí, Jordi, and Tommaso Monacelli. 2005. “Monetary Policy
and Exchange Rate Volatility in a Small Open Economy.”
Review of Economic Studies 72 (252): 707–34.
Gertler, Mark, Nobuhiro Kiyotaki, and Andrea Prestipino.
2017. “Wholesale Banking and Bank Runs in Macroeco-
nomic Modeling of Financial Crises.” NBER Working
Paper 21892, National Bureau of Economic Research,
Cambridge, MA.
Gertler, Mark, and Cara S. Lown. 1999. “The Information in
the High Yield Bond Spread for the Business Cycle: Evidence
and Some Implications.” Oxford Review of Economic Policy 15
(3): 132–50.
Gilchrist, Simon, and Egon Zakrajšek. 2012. “Credit Spreads
and Business Cycle Fluctuations.” American Economic Review
102 (4): 1692–720.
Giot, Pierre, and Sébastien Laurent. 2003. “Value-at-Risk for
Long and Short Trading Positions.” Journal of Applied Econo-
metrics 18 (6): 641–64.
Gorton, Gary, and Guillermo Ordoñez. 2014. “Collateral Cri-
ses.” American Economic Review 104 (2): 343–78.
Gruss, Bertrand. 2014. “After the Boom—Commodity Prices
and Economic Growth in Latin America and the Caribbean.”
IMF Working Paper 14/154, International Monetary Fund,
Washington, DC.
Hansen, Bruce E. 1994. “Autoregressive Conditional Density
Estimation.” International Economic Review 35 (3): 705–30.
Hatzius, Jan, Peter Hooper, Frederic S. Mishkin, Kermit
L. Schoenholtz, and Mark M. Watson. 2010. “Financial
Conditions Indexes: A Fresh Look after the Financial Crisis.”
NBER Working Paper 16150, National Bureau of Economic
Research, Cambridge, MA.
He, Zhiguo, and Arvind Krishnamurthy. 2013. “Intermediary
Asset Pricing.” American Economic Review 103 (2): 732–70.
International Monetary Fund (IMF). 2017. “Drivers of Capital
Flows and the Role of Investor Base in Latin America.”
Regional Economic Outlook: Western Hemisphere, May,
Washington, DC.
Izenman, Alan J. 2013. Modern Multivariate Statistical Tech-
niques: Regression, Classification, and Manifold Learning. New
York: Springer-Verlag.
Katagiri, Mitsuru. Forthcoming. “Macroprudential and Mone-
tary Policy in a Small Open Economy.” IMF Working Paper,
International Monetary Fund, Washington, DC.
Kiyotaki, Nobuhiro, and John H. Moore. 1997. “Credit Cycles.”
Journal of Political Economy 105 (2): 211–48.
Koenker, Roger. 2005. Quantile Regression. New York: Cam-
bridge University Press.
Komunjer, Ivana. 2013. “Quantile Prediction.” In Handbook
of Economic Forecasting, edited by Graham Elliott and Allan
Timmermann. Amsterdam: Elsevier.
Koop, Gary, and Dimitris Korobilis. 2014. “A New Index
of Financial Conditions.” European Economic Review 71
(C): 101–16.

118
G L O B A L F I N A N C I A L S T A B I L I T Y R E P O R T : I S G R O w T h A T R I S k ?
International Monetary Fund | October 2017
Korinek, Anton, and Alp Simsek. 2016. “Liquidity Trap
and Excessive Leverage.” American Economic Review 106
(3): 699–738.
Krishnamurthy, Arvind, and Tyler Muir. 2016. “How Credit
Cycles across a Financial Crisis.” Stanford University
Working Paper.
Lambert, Philippe, and Sébastien Laurent. 2002. “Modelling
Skewness Dynamics in Series of Financial Data Using
Skewed Location-Scale Distributions.” Discussion Paper,
Institut de Statistique, Université Catholique de Lou-
vain, Belgium.
Laurent, Robert D. 1988. “An Interest Rate–Based Indicator of
Monetary Policy.” Federal Reserve Bank of Chicago Economic
Perspectives 12 (1): 3–14.
Philippon, Thomas. 2009. “The Bond Market’s q.” Quarterly
Journal of Economics 124 (3): 1011–56.
Primiceri, Giorgio E. 2005. “Time Varying Structural Vector
Autoregression and Monetary Policy.” Review of Economic
Studies 72 (3): 821–52.
Stock, James H., and Mark W. Watson. 1989. “New Indexes
of Coincident and Leading Economic Indicators.” In NBER
Macroeconomics Annual 1989, edited by O. J. Blanchard and
S. J. Fischer. Cambridge, MA: National Bureau of Eco-
nomic Research.
———. 2003. “Forecasting Output and Inflation: The Role of
Asset Prices.” Journal of Economic Literature 41 (3): 788–829.
Tay, Anthony S., and Kenneth F. Wallis. 2000. “Density Fore-
casting: A Survey.” Journal of Forecasting 19 (4): 235–54.

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Global Financial Stability Report, October 2017
G
lobal Financial Stability R
eport Global Financial Stability Report
Wo r l d E c o n o m i c a n d F i n a n c i a l S u r v e y s
I N T E R N A T I O N A L M O N E T A R Y F U N D
17OCT
IM
F
O C T
17
Is G
row
th at R
isk?
Is Growth at Risk?

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