A Comprehensive Multi-Sector Tool For Analysis of Systemic Risk

WP/18/14
A Comprehensive Multi-Sector Tool for Analysis of
Systemic Risk and Interconnectedness (SyRIN)
By Fabio Cortes, Peter Lindner, Sheheryar Malik, and Miguel Angel Segoviano
IMF Working Papers describe research in progress by the author(s) and are published
to elicit comments and to encourage debate. The views expressed in IMF Working Papers
are those of the author(s) and do not necessarily represent the views of the IMF, its
Executive Board, or IMF management.
WP/18/14
© 2018 International Monetary Fund
IMF Working Paper
Monetary and Capital Markets Department
A Comprehensive Multi-Sector Tool
For Analysis of Systemic Risk and Interconnectedness (SyRIN)
Prepared by Fabio Cortes, Peter Lindner, Sheheryar Malik, and Miguel Angel Segoviano 1
Authorized for distribution by Udaibir S. Das
January 2018
IMF Working Papers describe research in progress by the author(s) and are published to
elicit comments and to encourage debate. The views expressed in IMF Working Papers are
those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board,
or IMF management.
Abstract
This paper presents the Systemic Risk and Interconnectedness (SyRIN) tool. SyRIN allows a
comprehensive assessment of systemic risk via quantification of the impact of risk
amplification mechanisms, due to interconnectedness structures across banks and other
financial intermediaries—insurance, pension fund, hedge fund and investment fund sectors,
which cannot be captured when analyzing sectors independently. The tool produces various
metrics to evaluate systemic risk from complementary perspectives, including tail risk, crossentity interconnectedness and the contribution to systemic risk by different entities and
sectors. SyRIN is easily implementable with publicly available data and can be adapted to
cater to different degrees of institutional granularity and data availability. The framework is
designed to be a tool to identify vulnerabilities from a top-down perspective that can lead to
deeper analysis in specific sectors for policy formulation.
JEL Classification Numbers: C02, C19, C52, C61, E32, E43, E44, G21
Keywords: Financial stability; systemic risk; shadow banks; mutual funds; spillovers.
Author’s Email Addresses: fcortes@imf.org, smalik2@imf.org, plindner@imf.org,
msegoviano@imf.org
1
The authors are grateful for the useful comments and contributions from E. Biffis, A. Bouveret, J. Gieck,
C. Goodhart, P. Hartmann, H. Huang, R. Kosowski, H.Q. Li, N. Liang, M. Lovaglio, and D. Schoemaker.
Special thanks to Felipe Nierhoff for his invaluable research assistance.
2
Contents
Page
Glossary .....................................................................................................................................4
I. Introduction ............................................................................................................................5
II. Channels of Interconnectedness ............................................................................................8
A. Investment Funds ......................................................................................................8
B. Hedge Fund Sector ..................................................................................................14
C. Insurance Sector ......................................................................................................17
D. Pension Funds .........................................................................................................20
III. SyRIN: A Comprehensive Multi-Sector Tool ...................................................................22
A. Tail Risk Indicators .................................................................................................24
B. Interconnectedness Indicators .................................................................................25
C. Systemic Loss Indicators.........................................................................................27
IV. A Tool for Financial Analysis ...........................................................................................27
A. Implementation .......................................................................................................28
B. Tool for Comprehensive Analysis...........................................................................32
C. SyRIN: A Tool for Guiding Deeper Analysis, and Related Policy
Recommendations ........................................................................................................39
V. Conclusions .........................................................................................................................45
References ................................................................................................................................46
Tables
1. Distress Dependence Matrix ................................................................................................25
Figures
1. Asset Liquidation and Direct Exposure Channels ...............................................................10
2. Asset Liquidation and Direct Exposure Channels ...............................................................11
3. Holdings of Nonbank Financial Institutions as of 2016 ......................................................11
4. US Funds as of 2016Q4 .......................................................................................................12
5. Channels for Hedge Fund Distress Transmission ................................................................16
6. Holdings of US Insurance Companies as of 2016Q4 ..........................................................19
7. US Pension Fund Holdings ..................................................................................................22
8. SyRIN: A Comprehensive Multi-Sector Tool .....................................................................24
9. Correlation of Returns: High Yield Mutual Funds and ETFs vs. the High Yield Index .....29
10. PoDs Funds ........................................................................................................................31
11. Systemic Risk Index ..........................................................................................................32
12. Financial Stability Index ....................................................................................................32
13. Marginal Contribution to Systemic Risk as of 2013Q4 .....................................................33
14. Increase in MCSR ..............................................................................................................34
14. Contributions to Distress Vulnerability of the Banking Sector .........................................34
3
16. Contributions to Banking Sector Distress Vulnerability as of 1Q2014 .............................35
17. Contributions to Insurance Sector Distress Vulnerability .................................................36
18. Contributions to Distress Vulnerability of the Banking (left) and Insurance (right) Sectors
as at January 2008 and March 2014 .........................................................................................37
19. Comparison of Systemic Risk Level with Contribution to Banking Sector Distress
Vulnerability ............................................................................................................................38
20. Comparing Aggregate Systemic Risk level with Contribution to Insurance Sector Distress
Vulnerability ............................................................................................................................38
21. Accommodative Monetary Policies Have Encouraged Greater Risk-Taking ...................40
22. Days Required for Full Liquidation ...................................................................................41
23. The Rise of Passive Investment in US Equity Markets .....................................................45
Appendix
I. Inputs for Implementation ....................................................................................................50
Appendix Tables
1. Input Series for Portfolio Reconstruction ............................................................................51
2. Input Series for Total Asset Data .........................................................................................52
4
GLOSSARY
CDS
CIMDO
DB
DC
DiDe
EF
ETFs
FSB
FSI
FSMD
HF
LTCM
MCSR
MMFs
NAV
PoD
PRA
SRMs
SyRIN
VaR
VI
Credit Default Swap
Consistent Information Multivariate Density Optimizing
Defined Benefit
Defined Contribution (DC)
Distress Dependence Matrix
Equity Funds
Exchange-traded funds
Financial Stability Board
Financial Stability Index
Financial system multivariate density
Hedge Funds
Long-term capital management
Marginal contribution to systemic risk
Money market funds
Net asset value
Probabilities of Distress
Prudential Regulatory Authority
Systemic risk metrics
Systemic Risk and Interconnectedness
Value at Risk
Vulnerability Index
5
I. INTRODUCTION
The global financial crisis demonstrated the speed and magnitude by which financial losses
can propagate through multiple corners of financial systems. The crisis showed that initial
losses in specific firms and markets can be magnified and transmitted across other financial
intermediaries and markets through systemic risk amplification mechanisms with dire
consequences for financial and economic stability. 2 Therefore, proper measurement of
systemic risk requires accounting for interconnectedness across financial entities and
markets beyond the banking system in order to incorporate the effects of systemic risk
amplification mechanisms that lead to financial contagion. 3
The proposed tool allows for a comprehensive assessment of systemic risk. The framework
derives metrics for assessing systemic risk from complementary perspectives and spans
banks and nonbanks. In comparison to the empirical financial risk literature that limits its
focus to the banking sector, or a much narrower definition of the financial system and
specific metrics of systemic risk, the SyRIN tool proposed in this paper allows for the
quantification of the impact of systemic risk amplification mechanisms due to
interconnectedness across banks and other financial intermediaries, including the insurance,
pension fund, hedge fund, and investment fund sectors, which cannot be captured when
analyzing sectors independently.4 The impact of systemic risk amplification mechanisms is
embedded in various systemic risk metrics (SRMs) that make it possible to evaluate systemic
risk from complementary perspectives, including the monitoring of tail (extreme) risk,
interconnectedness across different entities, and the contributions made to systemic risk by
different entities and sectors. While SyRIN is designed to be a tool for performing a
comprehensive top-down analysis that can lead to deeper scrutiny of sectors identified as
vulnerable, the tool is a reduced form approach; hence it cannot be used to identify the
particular causal channels that impact such sectors.5
2
Systemic risk is defined as “the risk of widespread disruption to the provision of financial services that is
caused by an impairment of all or parts of the financial system, which can cause serious negative consequences
for the real economy” (BIS and others 2016, IMF 2013).
3
Financial externalities that have the potential to amplify shocks up to the point of disrupting financial
intermediation cause systemic risk. Such externalities may themselves be impelled by cyclical and structural
vulnerabilities (Adrian and others 2014), which drive the interconected structures among diverse financial
intermediaries and markets, and pave the way for financial contagion.
4
An alternative framework is proposed by Duffie (2011), who recommends incorporating the largest banks, the
largest asset classes, and the largest counterparties into the monitoring of systemic risk.
5
For an overview of other methods considering interconnectedness and systemic risk measurement, please refer
to Malik and Xu (2017). However, most methods usually focus on a few sectors, commonly the banking or the
banking and insurance sectors.
6
As structural changes in financial intermediation are shifting the focus of risk to the nonbank
financial sector, a comprehensive assessment of systemic risk is of key importance. In the
aftermath of the global financial crisis, structural changes in financial systems are creating
different types of risks and joint vulnerabilities across sectors in the system, which cannot be
identified when scrutinizing intermediaries and sectors independently. Furthermore, banks
interact with other intermediaries, which might respond to stress in different ways, acting as
amplifiers and/or dampeners, depending on the state of their cycles. Hence, a proper policy
response requires a comprehensive measurement of systemic risk and interconnectedness
across sectors.6
SyRIN characterizes financial systems as portfolios of financial entities/sectors. Under this
characterization, the expected implied asset values of the entities making up the system and
the association across entities’ asset values (interconnectedness structure) can be represented
by a financial system’s multivariate density (FSMD). 7 It is this multivariate dimension of
SyRIN that allows analysis to estimate complementary and consistent SRMs that account for
interconnectedness across the asset values of the financial entities that make up the system.
SRMs can be estimated from various statistical moments of the FSMD. Importantly, because
such metrics are all derived from a common FSMD, the proposed metrics are consistent
across each other.
Interconnectedness across financial entities’ asset values is marked by direct and indirect
interlinkages across financial intermediaries and markets. While the business models of
different intermediaries expose them to multiple and diverse vulnerabilities, the transmission
of shocks across financial systems happens through two types of interlinkages, or channels of
contagion: direct and indirect. 8 Direct interlinkages primarily stem from contractual
obligations between financial entities. Indirect interlinkages can be caused by exposures to
common risk factors and market price channels, including asset fire sales (triggered by
stressed entities) and asset sell-offs (due to information asymmetries across agents). These
6
This is highly relevant for macroprudential policy, which aims to contain risks across the financial system as a
whole (BIS and others 2016). Since banks are usually the key providers of credit to the economy,
macroprudential policy has typically applied its policy levers to the banking system. However, as activity can
migrate into non-banks, macroprudential policy also needs to consider the systemic risk that can build up from
activities outside the banking system and develop policy responses to contain such risk (FSB 2011 and IMF
2013).
7
Such density characterizes (i) information of the individual firm’s value distributions, in its marginal densities;
and (ii) information of the function that describes the association across firm values (interconnectedness
structure), in its copula function. In contrast to correlation, which only captures linear dependence, copula
functions characterize linear and non-linear dependence structures embedded in multivariate densities.
8
These channels of contagion are also referred to as the direct exposure channel and the asset liquidation
channel, and have been highlighted in reports by the FSB and the Office of Financial Research as the main
transmitters of systemic risk across different sectors of financial systems. (FSB 2014).
7
can become self-reinforcing, possibly giving rise to non-linear increases in the magnitude
and speed of propagation of losses observed during financial crises. Indirect
interconnectedness might not be apparent during calm periods, but can take on greater
relevance in periods of high volatility. Hence, interconnectedness is complex and likely
unstable in periods of financial distress. Thus, the understanding of the intricacies of
interconnectedness between financial intermediaries and data constraints imposes significant
impediments to modelers for the estimation of FSMDs.
We model the FSMD using the Consistent Information Multivariate Density Optimizing
(CIMDO) approach.9 Rather than relying on calibration of parametric multivariate densities,
which can be problematic under the data restrictions available for systemic risk
measurement, the CIMDO approach allows the inference of multivariate densities and
interconnectedness structures across financial entities that are consistent with empirically
observed probabilities of distress (PoDs). Thus, when PoDs are estimated with market-based
information, SRMs estimated from the CIMDO-density incorporate changes in
interconnectedness structures (for example, direct and indirect interconnectedness) that are
consistent with markets’ perceptions of risk; hence, SRMs embed realistic market reactions,
an important feature for properly quantifying the non-linear increases in contagion usually
observed in crisis times.10
SyRIN is easily implementable and can be adapted to cater to a high degree of institutional
granularity and data availability. The portfolio assumption provides for the easy
incorporation of multiple sectors into the analysis. Moreover, implementation can be done
with market-based data or with publicly available supervisory data. This feature allows an
assessment of vulnerabilities developing in sectors where data may be scarce and which are
undergoing structural changes. We discuss how implementation can be performed under
different types of data.
SyRIN is an informative tool that, through top-down analysis, identifies specific entities and
sectors that may require closer scrutiny.11 To showcase the use of SyRIN, we present a case
9
The CIMDO methodology is based on the minimum cross-entropy approach (Kullback 1959). Under this
approach, a posterior multivariate distribution—the CIMDO density— is recovered using an optimization
procedure by which a prior density function is updated with empirical information via a set of constraints. In
this implementation, the empirical estimates of the PoD of individual banks act as the constraints, and the
derived CIMDO density is the posterior density that is the closest to the prior distribution and consistent with
these constraints. This methodology and its advantages relative to other parametric multivariate densities are
presented in detail in Segoviano (2006) and Segoviano and Espinoza (2017). CIMDO approach estimations are
robust under the probability integral transformation criteria (Diebold and others 1998).
10
While in most cases PoDs are estimated with market-based information, when such information does not
exist, PoDs can also be estimated with supervisory information. Section IV.A discusses these cases.
11
While the framework is reduced-form and cannot disentangle specific systemic risk amplification
mechanisms, SyRIN can help to identify sectors that might be highly vulnerable to such mechanisms. Thus, it is
a tool that can guide further analysis in specific vulnerable sectors, supporting efforts of authorities to define
adequate policy responses.
8
study that focused on the US financial system from 2007 to mid-2014. In this example, we
first used SyRIN as a tool for “top-down” analysis, which helped to identify the high yield
mutual fund sector as vulnerable by the end of the analysis period. Based on these findings,
we performed deeper analysis that found evidence of rising fragilities in the sector that made
the sector more vulnerable to large asset price movements. We then discuss structural
changes in capital markets that are currently ongoing and have the potential to increase
interconnectedness across specific sectors, hence possibly increasing systemic risk
amplification within such sectors. We argue that SyRIN could be used to assess the potential
implications of such structural changes. Nevertheless, the tool cannot be used to identify the
causes of such changes, since SyRIN is a reduced form approach.
The paper proceeds as follows. Section II describes the direct and indirect interlinkages
across financial entities and sectors that define their channels of asset value
interconnectedness. Section III describes the SyRIN. It presents its theoretical underpinnings
and defines the various measures of systemic risk and interconnectedness that can be
produced by the tool. Section IV discusses implementation aspects and presents analysis
performed on the US financial system to showcase the use of SyRIN. Section V concludes.
II. CHANNELS OF INTERCONNECTEDNESS
Structural changes in financial systems are creating sources of joint vulnerabilities across
various sectors in the system, vulnerabilities that cannot be captured when analyzing sectors
independently. These changes have led to greater sensitivity and synchronization
(interconnectedness) of asset price movements across sectors, increasing overall market and
liquidity risk. In this section, we briefly describe the generic characteristics of relevant
sectors in financial systems, comment on key structural changes in these sectors and describe
direct and indirect channels of interconnectedness and spillovers across sectors.
A. Investment Funds
While different types of investment funds are subject to diverse risk factors, these
intermediaries can transmit shocks to the financial system through two common channels.
These are the asset liquidation channel and the direct exposure channel. Those two channels
have been highlighted by the Financial Stability Board (FSB) and the Office of Financial
Research as the main transmitters of systemic risk arising from funds.
However, despite similar transmission channels, different investment fund sectors face
diverse vulnerabilities, since they are affected by different risk factors. Open-end funds (also
known as mutual funds) face a redemption risk that closed-end funds are not subject to,
because for the latter, investors cannot redeem shares in the short run due to long lock-up
periods. However, closed-end funds are subject to other vulnerabilities. The reason for this is
that, due to lack of redemption risk, closed-end funds can and often do hold more illiquid
9
assets, and more derivatives, and have more leverage than open-end funds. This implies that
they face higher liquidity risk, higher volatility risk and risk from deleveraging. Moreover,
closed-end funds exhibit discount as measured by the difference between the net asset value
(NAV) and the share prices. In crisis times the share prices of a closed end fund can therefore
fall significantly further than the value of an equivalent open-end portfolio. This may cause
fund managers to sell assets even if there is no redemption risk from investors. Figure 1
provides a summary of channels of contagion, risks, and risk factors affecting different types
of funds.
Open-end funds are exposed to redemption risk, as investors can redeem their shares (usually
daily) while the funds may have invested in illiquid securities. This exposes the fund to a
maturity and liquidity mismatch. Market stress can lead to losses on the funds’ assets, which
can provoke a run by investors due to the poor performance of the fund. 12 This run can be
exacerbated by the fact that fund flows follow performance and that there is a first mover
advantage incentive for investors to redeem ahead of others (Feroli and others 2014). Faced
with significant redemptions, asset managers are compelled to liquidate funds’ assets to meet
these demands, which could trigger fire sales. Some of the assets of the funds may be
illiquid, implying large discounts for investors and holders of those assets. Thus, fire sales
can impact the entire market through the asset liquidation channel, and further increase the
losses because of adverse price shocks (Figure 2). Nevertheless, open-end mutual funds
currently dwarf closed-end funds globally. 13
12
The suspension of redemptions by several U.K. retail property funds in July 2016 highlights the risks of liquidity
mismatches in certain open-ended funds. The temporary suspensions came after outflows accelerated following
the U.K. referendum to leave the EU. The funds that suspended redemptions eventually reopened, but only after
cutting valuations significantly and selling properties under adverse conditions.
13
For example, by end-2015, the number of assets under management of closed-end funds was less than
2 percent of the U.S. fund industry.
10
Figure 1. Asset Liquidation and Direct Exposure Channels
Source: Authors’ calculations.
Note: While different investment funds are subject to diverse vulnerabilities and risk factors, these sectors can transmit shocks to the financial system
through two common channels: the asset liquidation channel and the direct exposure channel. While the channels can broadly be defined into these two
categories, it is noted that the notion of market sentiment –as a potential risk/volatility amplifier – is implicit within the former. By definition, open-end
funds face a redemption risk that closed-end funds are not subject to, as the latter involve share sales and purchases between other shareholders on an
exchange, while in the former, units are bought from and sold to the fund. There is evidence of price impact from open-end mutual funds to asset markets,
especially in bond markets, less liquid assets, markets with herding, and emerging markets. This is particularly likely to be the case when there is a liquidity
mismatch between assets and more liquid liabilities. Money market funds can affect other financial institutions through the direct exposure/funding channel.
When faced with market stress that results in losses and outflows, funds are likely to reduce their exposures to risky issuers, thereby spreading risk in the
system. Closed-end funds, due to lack of redemption risk, can and often do hold more illiquid assets, more derivatives, and have more leverage than openend funds. This implies that they face higher liquidity risk, and risk from deleveraging as well as higher volatility risk. Moreover, closed-end funds often
trade at a discount as measured by the difference between NAVs and share prices. In crisis times the share prices of a closed-end fund can therefore fall
significantly further than the value of an equivalent open-end portfolio. This may cause the fund manager to sell assets, even if there is no redemption risk
from investors. Hedge funds’ ability to invest in derivatives, engage in buying on margin, and employ short-selling strategies and the use of leverage, as well
as invest in illiquid assets, makes them potentially more vulnerable to downward asset price spirals than traditional mutual funds. It is important to note that
transactions involving margin and collateral can exacerbate vulnerabilities. A short sale and a futures position involve a margin payment, and it is possible
that an adverse price coincides with (i) an increase in the margin requirement and (ii) a decrease in the value of collateral. This makes the margin call worse
compared to a situation where the margin does not change. The dynamics of asset prices must be considered together with the dynamics of margin
requirements over time, as the two move together.
11
Figure 2. Asset Liquidation and Direct Exposure Channels
Asset liquidation
channel
Direct exposure
channel
Losses on the asset
side
Market stress
Redemptions from
investors
Funding issues for
banks and CP issuers
Asset fire sales to cope
with redemptions
Source: Authors’ calculations.
The asset liquidation channel of open-end funds is likely to be important given their
significant market footprint. As of 2016Q4, these funds and exchange-traded funds (ETFs)
held $17.5 trillion of equities and fixed-income instruments (according to the Federal
Reserve). Their share of the US equity market amounted to 34 percent, while their holdings
of different debt market sectors ranged from 11 to 24 percent (Figure 3). They represented
the largest group of holders in the markets for agency- and mortgage-backed securities, as
well as for municipal bonds. The importance of mutual funds has been growing significantly
since 2006, especially for corporate and municipal securities (Figure 4).
Figure 3: Holdings of Nonbank Financial Institutions as of 2016Q4
(in percent market size)
60
50
Funds 1/
Pension Funds
Insur ers
40
30
20
10
Debt
securities
Treasury
securities
Agency- and
GSE-backed
securities
Munic ipal
securities
Source: Flow of Funds, authors’ calculations.
Note: 1/ “Funds” include mutual funds, closed-end funds, and ETFs.
2/ Share of publicly traded US equities.
Corporate
and foreign
bonds
Equities 2/
12
Figure 4. Holdings of US Funds as of 2016Q4
(in percent of each asset class)
35
30
25
20
15
10
5
Equities (rhs) 2/
Treasuries
Corp. & Foreign Bonds
Agencies and MBS 1/
2016:Q3
2016:Q1
2015:Q3
2015:Q1
2014:Q3
2014:Q1
2013:Q3
2013:Q1
2012:Q3
2012:Q1
2011:Q3
2011:Q1
2010:Q3
2010:Q1
2009:Q3
2009:Q1
2008:Q3
2008:Q1
2007:Q3
2007:Q1
2006:Q3
2006:Q1
0
Municipal Securities
Sources: Flow of Funds, authors’ calculations.
Note: 1/ Debt and mortgaged-backed securities (MBS) issued by US agencies
2/ Share of publicly traded US equities.
Empirical studies have provided evidence of the significance of the asset liquidation channel
stemming from open-end funds. Looking at equity funds (EF) from 1980 to 2004, Coval and
Stafford (2007) show that widespread selling by distressed funds led to a surge in illiquidity
and significant downward pressure on the individual stocks sold. Hau and Lai (2012) have
analyzed the impact of distressed selling by mutual funds on the U.S. stock market during the
global financial crisis (July 2007–June 2009). They show that stocks that were mostly owned
by distressed funds experienced more negative returns during the crisis due to fire sales, and
estimate that distressed selling by mutual funds accounted for 10 percent of the 52 percent
crisis-related decline in the US stock market. They document two negative twin peaks due to
fire sales’ effects, in November 2008 and February 2009. Using quarterly data, Manconi and
others (2012) show that during the first stage of the financial crisis (June–December 2007),
funds that invested in securitized assets had to liquidate part of their portfolio because they
faced liquidity needs. Mutual funds started by liquidating their corporate bonds (instead of
their illiquid securitized bonds), thereby spreading the crisis. 14Anand and others (2013) also
report that during the peak of the financial crisis (September 2008–March 2009), institutional
14
As the authors note: “… our findings show that mutual funds with high liquidity needs that were left with
exposure to the now illiquid securitized bonds played a significant role in spreading the crisis from the
securitized bond market to the seemingly unrelated corporate bond market.”
13
investors such as pension funds and money managers reduced their liquidity provision, which
resulted in a surge in illiquidity, especially for risky stocks.
Open-end funds, and especially money market funds, can also affect other financial
institutions through the direct exposure/funding channel. When faced with market stress that
results in losses and outflows, funds are likely to reduce their exposures to risky issuers,
thereby spreading risk in the system. After the reforms to money market funds (MMFs) that
came into effect in 2016, the commercial paper holdings of MMFs fell by 70 percent between
2016Q1 and 2016Q4. However, MMFs are still the main providers of repo funding in the US,
accounting for around $800 billion as of 2016Q4 (23 percent of the repo market per US Flow
of Funds). With broker-dealers receiving $1.3 trillion of repo funding as of end-2016, a run
on MMFs similar to the one during the crisis could notably impair the capability of dealers to
intermediate, potentially leading to significant problems throughout all sectors of the capital
markets. Therefore, frictions in mutual fund lending can lead to the transmission of distress
across borrowers, especially among banks and their broker-dealer subsidiaries.
Empirical studies have provided evidence of this direct exposure channel stemming from
money market funds. During the European sovereign crisis, U.S. MMFs cut their exposures
to European banks, which seemed to be an important factor to explain a severe dollar
shortage for those banks, as documented by Correa and others (2012). This dollar shortage
was also visible in the large increase in euro dollar basis swaps, until the ECB and the U.S.
Federal Reserve reintroduced USDEUR swaps in November 2011. Chernenko and Sunderam
(2014) show that U.S. funds exposed to European banks suffered large outflows in mid-2011,
which led them to withdraw funding to non-European issuers, thereby spreading distress.
During the financial crisis, European MMFs also suffered losses related to their exposure to
securitized assets, and some had to suspend redemptions since their assets were too illiquid to
be priced accurately (Bengtsson (2012). In the United States, MMFs experienced massive
outflows in September 2008 after one MMF was not able to maintain its NAV close to one
dollar (the fund “broke the buck”), which amplified further the financial instability arising
from the Lehman collapse (Baba and others 2009).
Another potential transmission channel of risk from MMFs to other financial institutions
could occur through sponsor support, by purchasing troubled assets or by providing liquidity.
Even though sponsors of MMFs (asset managers or banks) do not have to step in to support
their funds, some of them provided direct support by purchasing the portfolio. For example,
Société Générale bought assets from its enhanced MMFs in the second half of 2007 and in
20081Q as investors sought redemptions and suffered losses in this portfolio. These
purchases resulted in a EUR 552mn loss for Société Générale. Barclays chose to guarantee
the par value of its MMFs, resulting in GBP 276mn in losses (Bengtsson 2012).
14
B. Hedge Fund Sector
Hedge funds (HF) can be described as private investment vehicles for wealthy and
financially sophisticated individuals and institutional investors. These primarily include
pension funds, insurance companies, and sovereign wealth funds. Compared to mutual funds,
HF are relatively unconstrained by regulatory oversight.15 Hedge funds’ ability to invest in
derivatives, engage in buying on margin, short-selling and using leverage, as well as
investing in illiquid assets, makes them potentially more vulnerable to downward asset price
spirals than traditional mutual funds. The greater freedom from constraints regarding risk and
leverage has provided HF with the ability to develop and use complex and proprietary
investment strategies in a variety of instruments. 16
Like mutual funds, HF can propagate risk through the asset liquidation channel. The collapse
of a hedge fund (or group) leads to the liquidation of their position at fire sale prices. The
impact on asset prices may be further magnified due to illiquidity and leverage. This would
mean that if positions were large relative to the liquidity of the asset, any potential disorderly
unwinding would result in losses for the holders of the assets, which can ultimately
contribute to distress at a systemic level. HF can obtain leverage in several ways, including
margin accounts, derivatives, repos, and short sales. It is important to note that transactions
involving margin and collateral can exacerbate vulnerabilities. A short sale and futures
position involves a margin payment, and it is possible that an adverse price coincides with
(i) an increase in the margin requirement; and (ii) a decrease in the value of collateral. This
makes the margin call worse compared to a situation where the margin does not change. The
dynamics of asset prices must be considered together with the dynamics of margin
requirements over time, as the two move together. This also implies that a hedge fund may
have funding problems, even though markets are liquid. 17 As a result of the above, it is
imperative for regulators and supervisors to be able to collect information not only on cash
assets but also derivatives positions, short sales, repurchase agreements, margins and
counter-parties. Two well-cited examples of hedge fund illiquidity problems were long-term
capital management (LTCM) and Amaranth Advisors. These funds had asset positions with
positive mark to market value, but were unable to meet margin calls. Figure 5 provides a
15
Some examples of regulation affecting mutual funds could be: (i) shares may be redeemable at any time (for
open ended-funds); (ii) NAV needs to be calculated daily; (iii) investment policies must be disclosed; and
(iv) the use of leverage is limited.
16
Trading strategies are typically dynamic, as compared to mutual funds, which usually deploy buy-and-hold
strategies.
17
Illiquidity encompasses market liquidity and funding liquidity. Market (asset) liquidity refers to the ability of
unwinding positions quickly with minimal price impact. Market liquidity is systemic, in that it may be reduced
during a financial disturbance. Funding liquidity, on the other hand, is the ability of an investor to obtain cash to
meet obligations. Funding liquidity is typically idiosyncratic to the firm.
15
stylized representation of the channels for distress transmission between HF and the rest of
financial system.
Overall, losses from HF during the financial crisis in 2008 were compounded by leverage
and the illiquidity of underlying holdings in certain asset classes. This was particularly the
case in credit and other fixed income markets, including syndicated loans, high yield bonds,
convertible bonds, emerging market debt, and credit derivatives. Several funds in these asset
classes, namely those primarily focused on relative value strategies that deploy material
amounts of leverage, suffered significant losses 18. These losses were quickly followed by a
material increase in redemptions from their investors, which led to various hedge fund
managers having to apply gates19 and suspend redemptions due to their inability to liquidate
the necessary number of assets to fully meet these outflows, while also protecting the
interests of those investors who remained in the fund. Therefore, hedge fund liquidation can
exacerbate market volatility and reduce liquidity in different sectors of financial markets. In
addition, HF are exposed to common market risk factors along with other sectors in the
financial system. Khandani and Lo (2011) report that in August 2007, long/short equity HF
experienced large losses due to the sudden liquidation of a fund that led to fire sales by funds
implementing the same types of strategy.
18
Credit strategies, such as distressed and convertible bond arbitrage, lost 19 percent and 26 percent,
respectively, while emerging markets HF lost 30 percent in 2008 (Le Sourd 2009). Per the authors, investors
were “given a painful reminder that HF are exposed to a variety of risk factors, such as credit risk, liquidity risk,
and several equity risk factors.”
19
Gates are measures to stop a specific amount of redemptions from a fund vehicle. Gates can take two forms:
(i) at the fund-level and (ii) at the investor level.
16
Figure 5. Channels for Hedge Fund Distress Transmission
Direct exposure channel
(liability side)
Refinancing issues for
Prime brokers
Funding risk for
hedge funds
Losses on the asset
side
Market stress
Redemptions from
investors
Asset fire sales to cope
with redemptions
Asset liquidation channel
Source: Authors’ calculations.
Note: Red arrows indicate that the effects are amplified by the use of leverage by hedge funds.
The direct exposure channel is also relevant for HF because of their relationship to banks
through prime brokerage services. Hedge funds have a symbiotic relationship with the
banking sector. Banks are exposed to hedge fund risk via prime brokerage services. Prime
brokers are banks that offer services to hedge funds. The bank handles the hedge fund’s
trades, and determines the collateral the hedge fund has to provide, in addition to borrowing
securities for short positions and providing loans. 20 Prime brokerage in the United States is
very concentrated. As of end-2006, the top 10 prime brokers serviced 84 percent of hedge
fund assets under management (King and Maier 2009). Prime brokers offer margin lending to
HF and in return can re-pledge the funds’ assets in the repo market to refinance the loan (rehypothecation). Therefore, losses faced by the HF on their assets reduce their ability to repay
the loan to the prime broker and thereby increase the refinancing risk for the prime broker, as
they must post margin calls to account for the decrease in value of the collateral they posted.
An additional channel can develop through exposure to common risk factors causing
simultaneous distress in all sectors. In such cases, HF may not be the source of the shock but
contribute to shock amplification, given their institutional structures. The dynamic and highly
competitive nature of hedge fund investment strategies implies that such entities will shift
their assets tactically and quickly, moving into markets when profit opportunities arise, and
moving out very quickly at the first sign of distress, which can amplify market gyrations. 21
20
21
Hedge funds are not directly regulated, but do need to report to prime brokers.
The fees charged by HF are dependent on performance and are, in general, higher than mutual funds. The fee
structure gives hedge fund managers the incentive to make profit, but also encourages risk-taking.
17
Although such tactics may benefit hedge fund investors in many cases, they can also cause
market dislocation in crowded markets, particularly if large asset shifts happen unexpectedly.
C. Insurance Sector
It is useful to think of the insurance business model as a sophisticated swap, in which
insurers receive fixed payments from policyholders and pay floating benefits. Indeed,
insurers raise premiums (from individuals, households, workers, and employers), which can
then be invested to earn a return, and pay benefits contingent on different event occurrences
(mortality, morbidity, casualty, and liability events). As insurance risks are largely
idiosyncratic and weakly correlated with the economic cycle, insurers can reap pooling and
diversification benefits and earn a spread between the fixed and the floating leg of the
swap.22 The insurance business model is different from that of banks: insurance companies
hold long term assets funded by short term liabilities, and should be more insulated from
wider market dislocations than the banking sector. This is because the prepaid funding model
(implemented via insurance premiums paid upfront and with penalties for discontinuing a
policy) offers a cushion to mitigate short-term liquidity needs. Moreover, the need to meet
future obligations tilts insurers’ asset allocation toward liability-driven investment, which
reduces the potential mismatch between assets and liabilities in periods of distress.
Insurance companies are subjected to various types of financial risk. Interest rate risk is
particularly material for long-term contracts with minimum guarantees. The nature of
insurance liabilities means that life insurers’ duration gap is typically negative. The gap is
managed and reduced by investing in fixed-income instruments, including government bonds
and fixed-income derivatives (for example, swaps and swaptions), as well as corporate bonds
and securitized products offering a more rewarding risk-return trade-off. Credit risk is
material for all the previous asset classes, and takes the form of counterparty risk in
reinsurance and derivative transactions. Depending on the business mix, life insurers can also
have exposure to the equity market, as well as to property and infrastructure assets offering a
good hedge against inflation. Inflation hedging is important for both life and non-life
insurers, due to indexation of benefits and the relationship of claims handling and settlement
processes to expense inflation. Foreign exchange risk can be material for insurers, but is
often mitigated by regulatory requirements that match foreign currency exposures with
suitable assets or hedging instruments on the asset side. Concentration risk typically affects
both the asset and the liability side, as insurers’ holdings may be concentrated in particular
asset classes, while liabilities may originate from a narrow range of exposures or
22
Insurance is characterized by an inverted production model: insurance premiums are received upfront and
used to build reserves (technical provisions) to meet future obligations (insurance benefits). The latter are
typically long term in life insurance, and short term in non-life insurance, although there are lines of business
(for example, professional liability) that are “long tail” due to the longer duration of the claims
reporting/settlement process.
18
sales/underwriting channels. Liquidity risk is material for insurers when unexpected claims
occurrences (for example, catastrophic events, waves of surrenders/lapses) cause immediate
liquidity needs that cannot be supported by the regular cash flow provided by insurers’ longterm investments.
Technical risks faced by insurers are related to underwriting performance and claims
experience. These risks hinge on the nature (for example, life versus non-life) and mix (for
example, mortality protection versus annuities) of the business. Claims experience can be
higher than anticipated due to random fluctuations in claims occurrence or the emergence of
systematic patterns of occurrence. The latter are particularly important for reserving risk,
meaning that loss provisions become inadequate to cover expected future claims. Systematic
risks that are typically stress tested are demographic and catastrophic risk occurrences (for
example, natural hazards and pandemics), which by their nature cut across several different
policies and lines of business and undermine the risk pooling model on which the insurance
business model is based.
Non-traditional activities undertaken by insurance companies may give rise to relevant
contagion channels to other financial intermediaries and markets. The insurance sector has
become increasingly non-traditional over time, by providing a variety of “bank-like”
functions such as corporate financing, or by engaging in securities financing transactions,
securities lending, derivatives writing, and collateral management. Such activities are
associated with the emergence of new risks, which may give rise to direct exposure and asset
liquidation channels in distress episodes.
The insurance sector has emerged as a vital source of financing for other sectors; hence, the
direct exposure channel may spread distress to such sectors. Insurance sector financing has
been channeled by either holding corporate bonds and commercial mortgages securities, or,
more recently, by providing direct loans to the corporate sector. Insurance companies have
become an important buyer of securitized product. This has contributed to the insurance
sector’s expanded role in the financial intermediation process. As of end-2016, life insurance
companies held almost $6.8 trillion in total assets, and property and casualty firms another
$1.9 trillion. This made them the largest U.S.-based corporate bond investor, with 25 percent
of total holdings, and a significant player in the illiquid municipal bond market, holding
13.5 percent of that market (Figure 6). Large-scale distress of insurance firms would inhibit
their ability to continue playing a “bank-like” role and provide financing to the corporate
sector.
19
Figure 6. Holdings of US Insurance Companies as of 2016Q4
(in percent)
30
25
20
15
10
5
0
Domestic & Foreign
Bonds
Commercial
Mortgages
Agencies
Treasuries
(Marketable)
Equities
Sources: Flow of Funds, authors’ calculations.
The asset liquidation channel can become an issue if insurers are exposed to liquidity needs
resulting in fire sales. Such situations may occur as the result of margin calls, due to nontraditional insurance operations, and, to a lesser extent, due to major catastrophes and waves
of lapses/surrenders of policies.23, 24 Margin calls can be related to insurers’ use of
derivatives, which is typically heavy when the business mix includes investments and
participating products linked with long-term guarantees. During the past decade, however,
insurers have increasingly dominated the supply side of the derivative market. Given the
long-term nature of their investments and the inventory of risk exposures they hold, they can
write non-standardized, long-term derivatives that are too costly for other financial
institutions to intermediate in the current regulatory environment. These activities have
extended to bank-like activities, such as liquidity provision in the form of liquidity or
collateral swaps. The liquidity needs arising from such transactions could pressure some
insurers, leading them to sell assets and thus create an important contagion channel,
especially if non-traditional insurance activities are significant.
23
Large aggregate claim amounts resulting from event occurrences affecting several policies simultaneously
(for example, pandemics, earthquakes) can deplete standard reserves and extra provisions (such as resilience
reserves), as well as eat into the regulatory capital buffer, forcing an insurer to sell illiquid, long-term assets at a
significant discount.
24
A policyholder can lapse by walking away from a contract (for example, a term assurance policy with no cash
value) or surrender a policy by partially or fully withdrawing the policy cash value (exit or surrender value). In
both cases, the insurer is exposed to losses resulting from lower business volume (for example, initial expenses,
overheads, asset management charges). In the case of cash values, minimum guarantees offered on surrender
benefits can be costly in a deflationary environment. An expansionary environment induces policyholders to
surrender policies to take advantage of more advantageously priced policies or alternative investment
opportunities. Waves of lapses or surrenders could lead to asset fire sales by insurers. The empirical evidence
on such bank-run-like behavior is limited.
20
Insurers may leverage capital via intragroup loans and the use of off-balance sheet
instruments. Such leverage can amplify shocks and spread contagion across sectors. These
concerns are shared by international regulators, who are actively working on these issues. 25
Additional indirect channels of contagion might be induced by insurance companies. For
example, the banking and insurance sectors seem to be connected via a “flow of funds
nexus,” between the corporate bond market and lines of credit. Acharya and Richardson
(2010) provide the following example: Suppose AA and AAA-rated firms find it punitively
expensive to issue corporate bonds, given the inability of insurance companies to play
corporate financing roles; they may then be forced to draw on their bank lines of credit. This
type of last-resort financing will amplify liabilities in the banking sector, leading to distress.
Acharya and Richardson suggest in their analysis that, whereas the banking sector has
become better capitalized and less risky (due to conscious behavior or regulation), we have
not witnessed a decline in systemic risk of the insurance sector.
D. Pension Funds
Depending on the nature of the pension promises, pension plans can be classified as Defined
Benefit (DB) or Defined Contribution (DC) plans. In DB plans, members pay regular
contributions to acquire the right to receive post-retirement benefits, whose value is
guaranteed and usually determined as a function of the number of years of contribution and
the (average) level of salary before retirement. However, current and future pension liabilities
can be supported by the contributions of active members. In other words, the plan does not
need to be fully funded. On the other hand, if the funding level is inadequate, the sponsor
must inject capital into the pension fund to ensure that future pension promises have proper
financial backing. Corrective measures could include an increase in pension contributions by
active members or the reduction in pension benefits to pensioners. In DC plans, the plan
liability is limited to the value of each member’s individual account into which he had
contributed. The account balance will evolve depending on the performance of the assets in
which the contributions are invested. The cumulative result of the contributions paid into the
account and the investment returns generated during the working life of a member
(accumulation period) will result in a cash balance that will be available at retirement to
support the individual during the decumulation period. By definition, a DC plan is fully
funded, and passes all the risk back to the participating members. Some pension plans may
present features of both DB and DC types, in which case they are referred to as hybrid plans.
25
Solvency II, for example, considers group supervision as an essential tool to supplement and complement the
supervision of individual companies, and provides a range of governance and reporting requirements to facilitate
group supervision.
21
Pension plans are in general unlikely to give rise to important contagion channels. DB plans
hold long-term assets, limit the use of derivatives to hedging, are prevented from borrowing,
and can rely on sponsors’ contributions as well as benefit reductions in case of necessity.
However, the extent to which DB plans can truly rely on additional sponsor contributions or
benefit reductions in situations of wider market distress is unclear. In DC plans, individual
members are the ultimate bearer of risk.
However, some contributions to systemic risk could materialize under certain conditions.
Herd behavior may exacerbate asset price swings and contribute to downward price pressure
in situations of market distress (for example, Impavido and Tower 2009, Broeders and others
2016).26 Similarly, some asset-liability management strategies are inherently procyclical and
may affect some asset classes, such as bond and equity markets. Waves of redemptions or
drops in contributions in response to poor investment performance may result in pension
plans being forced to liquidate illiquid asset holdings at large discounts, thus depressing
valuations and retention of members. The effects would be amplified in situations where any
minimum guarantees end up in the money without adequate backing assets. This contagion
channel is not typically material, as abrupt withdrawals before retirement from both DB and
DC plans are typically prevented or discouraged by steep penalties. 27 Unlike other sectors,
such as mutual funds and insurance companies, the holdings of riskier securities by pension
funds, particularly equities, have decreased significantly since 2008, while their investments
in risk-free assets such as US treasuries has increased moderately over the same period
(Figure 7).28
Moreover, changes in the behavior of pension funds can also have an (indirect) impact on the
overall risk of the financial system. This is because there is emerging evidence that pension
funds (alongside insurance companies) may be playing less of a countercyclical role in
financial markets, making it more difficult to provide liquidity in times of stress. 29 Increased
regulatory emphasis on asset-liability matching can play a role in making institutional
26
Broeders and others (2016), for example, document three types of herd behavior: (i) weak herding, whereby
pension plans follow a similar rebalancing behavior; (ii) semi-strong herding, meaning that pension plans
respond in the same way to exogenous shocks; and (iii) strong herding, whereby some plans intentionally
replicate the strategy of other funds.
27
There is evidence that changes to the regulatory framework, as well as to accounting standards, have
increasingly limited the risk-taking capacity of pension funds (Franzen 2010). Also, there is anecdotal evidence
that the lessons learned from the losses experienced by pension funds during the financial crisis in 2008 have
made pension funds increasingly less tolerant of losses, while also strengthening their risk management
processes.
28
While this is likely the case in the aggregate, there is also evidence that some US public pension funds may
have increased their risk taking over recent years. This may be related to the fact that US public funds face
distinct regulations that link the rate at which they discount their liabilities to their expected return on assets.
This contrasts with most other pension funds, which link the liability discount rate to the relative riskiness of
their promised pension benefits (Andonov and others 2013).
29
See discussion paper by the Bank of England and the Procyclicality Working Group: “Procyclicality and
structural trends in investment allocation by insurance companies and pension funds” (July 2014).
22
investors more procyclical. If these investors are minimizing the liability shortfall, they may
become increasingly risk averse during periods of stress, as their liability gap increases in
down markets. Therefore, they are less likely to behave as shock absorbers and, on the
contrary, more likely to sell securities during periods of stress, despite their overall reduction
in risk-taking capacity in recent years.
Figure 7. US Pension Fund Holdings
(in $ bn), as of 2016Q4
40
Global Equities
U.S. Treasuries
U.S. Corporate and Foreign Bonds
30
20
10
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Sources: Federal Reserve, authors’ calculations.
III. SYRIN: A COMPREHENSIVE MULTI-SECTOR TOOL
Most empirical literature on systemic risk measurement has tended to focus on a single
sector, typically the banking sector.30 Upper (2007) provides a survey of work on contagion
in the interbank market, within national banking systems. Segoviano and Goodhart (2009)
proposed measures of systemic risk in the banking sector. Theoretical literature includes
Allen and Gale (2000), who show that withdrawal of liquidity at one bank can affect the
entire banking system, depending on the shape of the network. Freixas, Parigi, and Rochet
(2000) discuss the resilience of a banking system to the insolvency of a single bank.
Research analyzing risks associated with nonbank financial institutions has recently started to
emerge. A few studies provide evidence of contagion in the insurance sector. Fenn and Cole
(1994) investigate the effects of contagion among life insurance companies. Fields and others
30
In their survey or systemic risk analytics, Bisias and others (2012) note that “relatively few of the studies in
our sample deal directly with pension funds or insurance companies despite the fact that the recent crisis
actively involved these institutions.”
23
(1998) show that news of large losses faced by Lloyd’s in 1993 had negative effects on
US insurers. Polonchek and Miller (1999) provide further evidence of contagion effects
within the insurance industry by analyzing the impact of equity offerings on the sector. 31
Contagion in mutual funds has been considered by Chernenko and Sunderam (2014), and
Hau and Lai (2012). These papers document how mutual funds can spread shocks to the
entire financial system. Billio and others (2012) analyze systemic risk among banks, brokerdealers, hedge funds, and insurance companies. 32
SyRIN conceptualizes the financial system as a portfolio of entities, spanning banks, and
nonbanks. In providing a comprehensive treatment of both bank and nonbank financial
institutions/sectors, the proposed tool is—to the best of our knowledge—the first of its kind
in the literature on systemic risk analysis that attempts to incorporate banks and nonbanks in
a consistent manner. The tool, illustrated in Figure 8, incorporates the largest banks and
insurance companies operating in a country, as well as the pension sector, mutual fund
sector, and hedge fund sectors.
By treating the financial system as a portfolio of entities, this tool incorporates asset value
interconnectedness across banks and nonbanks within the measurement of systemic risk. The
incorporation of interconnectedness is done thorough the inference of the FSMD, which in
SyRIN is done with the CIMDO approach, a non-parametric method which enables robust
inference of the FSMD (that is, the CIMDO-multivariate density) from minimum information
on asset price returns and PoDs of the entities that make up the financial system (Box 1).
The FSMD allows us to estimate a set of systemic financial stability measures, permitting the
assessment of financial stability from different yet complementary perspectives. These
measures include financial stability indicators and systemic loss indicators. The financial
stability indicators are estimated from joint and conditional probabilities from the FSMD and
include (i) the Financial Stability Index (FSI)—a tail risk indicator, and (ii) the Distress
Dependence Matrix (DiDe) and Vulnerability Index (VI)—both interconnectedness
indicators (Segoviano and Goodhart 2009). 33 Additionally, this approach allows for the
estimation (via simulation) of losses at the systemic level, from which we estimate the
systemic loss indicators. These include (i) the marginal contribution to systemic risk (MCSR)
and (ii) the Systemic Risk Index. These are indicators that simultaneously account for
interconnectedness and the relative size of each entity in the system.
31
See Egginton and others (2010), who document how contagion effects arose during the collapse of AIG.
32
They employ Principle Component Analysis to measure commonality in returns across institutions. Using
Granger causality tests, the authors identify the direction of the relationships among institutions and show that
during the global financial crisis, the number of interconnections between financial institutions soared, with
banks and insurance companies being central to the transmission of shocks to other institutions.
33
In this paper, as an illustrative application, we implement the FSI, DiDe, and Vulnerability Index. The FSMD
allows us to estimate additional indicators based on different conditional and joint probabilities.
24
For illustration purposes, we proceed by defining a financial system comprising three
entities. The asset values of the portfolio of entities are characterized by the random variables
x, y, and r . Hence, following the procedure described in Box 1, we infer the CIMDO-density
function, which takes the form:
( , , ) = ( , , )exp − 1 + ̂ +
,
+
,
+
,
,
(1)
where, ( , , ) and ( , , ) ∈ ℝ .
Figure 8. SyRIN: A Comprehensive Multi-Sector Tool
Source: Authors’ calculations.
A. Tail Risk Indicators
The Financial Stability Index (FSI)
The FSI reflects the expected number of entities becoming distressed given that at least one
entity has become distressed. The FSI represents a probability measure that predicates any
entity becoming distressed, without indicating the entity. 34 A higher number signifies
increased instability. The FSI embeds not only changes in the individual entities’ PoDs, it
also captures changes in the distress dependence among the entities in the system, which
34
The FSI is based on the conditional expectation of a default probability measure developed by Huang (1992),
who shows that this measure can also be interpreted as a relative measure of banking linkage. When the FSI=1.0
in the limit, banking linkage is weak (asymptotic independence). As the value of the FSI increases, banking
linkage increases (asymptotic dependence). For empirical applications, see Hartmann and others (2001).
25
increases in times of financial distress; therefore, in such periods, the financial system’s FSI
may experience larger and non-linear increases. For example, for a system of two entities, the
FSI is defined as follows:
P X  xdx  P Y  xdy
.
FSI 
(2)
1  P X  xdx , Y  xdy


 


B. Interconnectedness Indicators
Distress Dependence Matrix (DiDe)
For each period under analysis, and for each pair of entities in the portfolio, we estimate the
set of pairwise conditional probabilities of distress, which are presented in the DiDe. This
matrix contains the PoD of the entity specified in the row, given that the entity specified in
the column becomes distressed. While conditional probabilities do not imply causation, this
set of pairwise conditional probabilities can provide important insights into
interconnectedness and the likelihood of contagion between the entities in the system. For the
hypothetical financial system defined in Eq. (1), at a given date, the DiDe is represented in
Table 1.
Table 1. Distress Dependence Matrix
Financial Entity X
Financial Entity Y
Financial Entity R
Financial Entity X
1
P(Y/X)
P(R/X)
Financial Entity Y
P(X/Y)
1
P(R/Y)
Financial Entity R
P(X/R)
P(Y/R)
1
Source: Authors’ calculations.
Here, for example, the PoD of entity X conditional on entity Y becoming distressed is given
by,
P X  xdx , Y  xdy
x
y
P X  xd Y  xd 
.
(3)
P Y  xdy






Vulnerability Index
The Vulnerability Index (VI) is a measure constructed using information contained within the
distress dependence. For example, the VI for entity X (given the above 3 entity example/
notation) is defined as sum of joint probabilities, i.e.
( )=
( ≥
| ≥
)∙
≥
+ ( ≥
| ≥
)∙ ( ≥
) (4)
The expression (4) would be computed at each point in time and similarly for the other
entities in the system.35 Typically the measure is normalized by the maximum over the
monitoring horizon in order to map the measure in the space [0, 1].
35
We are assuming that the three entities are in fact a subset of the entire universe of entities in the financial
system. VI(X) would tend to ( ) as we sum over joints vis-à-vis all entities in universe.
26
Box 1. The CIMDO Framework to Model Multivariate Densities
The original formulation of CIMDO is presented in Segoviano (2006). Further methodological
improvements and robustness proofs are presented in Segoviano and Espinoza (2017).* CIMDO is based
on the Kullback (1959) minimum cross-entropy approach. For illustration purposes, we focus on a portfolio
containing two different types of assets (financial system sectors in this application), whose logarithmic
returns are characterized by the random variables x and y. Hence, we define the CIMDO-objective function
as:
( , )
, = ∬ ( , ) ln
, where ( , ) and ( , ) ∈ ℝ .
(a)
( , )
The prior distribution follows a parametric form: ( , ); for example, a multivariate t distribution that is
consistent with economic intuition (default is triggered by a drop in the firm’s asset value below a threshold
value) and with theoretical models (the structural approach to model risk). However, the parametric density
( , ) is usually inconsistent with the empirically observed measures of distress. Hence, the information
provided by the empirical measures of distress of each bank in the system is of prime importance for the
recovery of the posterior distribution. In order to incorporate this information into the posterior density, we
formulate consistency-constraint equations that have to be fulfilled when optimizing the CIMDO-objective
function. These constraints are imposed on the marginal densities of the multivariate posterior density, and
are of the form:
( , )
=
,
,
( , )
,
=
( )
where ( , ) is the posterior multivariate distribution that represents the unknown to be solved.
and
are the empirically observed probabilities of distress (PoDs) of each of the sectors in the system,
and
are indicating functions defined with the distress thresholds , , estimated for each
, ,
,
sector in the portfolio. In order to ensure that the solution for ( , ) represents a valid density, the
conditions that ( , ) ≥ 0 and the probability additivity constraint ∬ ( , ) = 1 also need to be satisfied.
Once the set of constraints is defined, the CIMDO-density is recovered by minimizing the functional:
,
=
( , ) ln ( , )
( , ) ln ( , )
−
+
( , )
,
−
+
( , )
,
−
( )
+
( , )
−1
where , represent the Lagrange multipliers of the consistency constraints and represents the Lagrange
multiplier of the probability additivity constraint. By using the calculus of variations, the optimization
procedure can be performed. Hence, the optimal solution is represented by a posterior multivariate density
that takes the form
( , ) = ( , )exp − 1 + ̂ +
+
( )
,
,
From the functional defined in equation (c), it is clear that the CIMDO recovers the distribution that
minimizes the probabilistic divergence, that is, “entropy distance,” from the prior distribution and that is
consistent with the information embedded in the moment-consistency constraints. Thus, out of all the
distributions satisfying the moment-consistency constraints, the proposed procedure provides a rationale by
which we select the posterior that is closest to the prior (Kullback 1959), thereby, solving the underidentification problem faced when trying to determine the unknown multivariate distribution from partial
information provided by the PoDs in its marginals. When we solve for the CIMDO-density, the problem is
converted from one of deductive mathematics to one of inference involving an optimization procedure. This
is because, instead of assuming that parametric probabilities characterize information contained in the data,
the proposed approach uses this information to infer values for the unknown multivariate probability
density.
* In comparison to the original version, the current version of the CIMDO approach employs a multivariate T density as
a prior density, which improves robustness and includes a computational algorithm that allows estimation of CIMDO
densities of large dimensions.
27
Contributions to Distress Vulnerability
While the VI is a useful summary measure to quantify distress dependence of an entity vis-àvis other entities in the system, we suggest that the contributions of individual entities to the
overall VI is even more informative. Percentage contributions to distress dependence of
entity X from entity Y, for example, would simply be:
( ≥
| ≥
)∙
≥
x 100%
(5)
( )
Monitoring these contributions over time provides a metric of how distress dependence of an
entity in relation to different entities (or sectors) in the system has been evolving.
C. Systemic Loss Indicators
Marginal Contribution to Systemic Risk (MCSR)
The MCSR requires simulation of the distribution of losses at the system level. From this
distribution, a systemic tail risk measure is delineated which, for this analysis, will be the
“expected shortfall” (ES)36. The MCSR for each sector (or entity) is backed out from the
systemic ES using a Shapley-value-based risk attribution methodology proposed by
Tarashev, Borio, and Tsatsaronis (2010).
Systemic Risk Index
This is constructed using the systemic ES recorded at each point in time. The resulting series
is bound between zero and unity by deflating by the max ES recorded over the period (or
sub-period). We suggest that such a normalization is informative given that it illustrates the
relative position of systemic risk with respect to a reference point.
These indicators allow the analysis of complex interlinkages and the quantification of
vulnerability to distress risks between entities, both within and across sectors. Hence, this
framework is a useful tool for addressing the following key questions: (i) How is systemic risk
evolving and what is its current level? (ii) What are the institutions/sectors that contribute most
to systemic risk? (iii) How vulnerable are specific institutions/sectors to distress in other
institutions/sectors?
IV. A TOOL FOR FINANCIAL ANALYSIS
In this section, we present a case study that focuses on the US financial system from 2007 to
mid-2014 in order to showcase the use of SyRIN. We first show how the tool can be
36
The ES represents the (average) extreme loss to the system that occurs with a probability of 1percent (or less).
28
implemented. Then we use SyRIN to assess (i) the evolution of systemic risk in the
U.S. system; (ii) what are the institutions/sectors that contribute most to systemic risk; and
(iii) how vulnerable are specific sectors to distress in other sectors. The tool indicates that at
the time of analysis, the high yield mutual fund sector showed high systemic risk impact;
hence, we performed further scrutiny in this sector to try to understand why amplification
mechanisms could develop in this sector. 37
A. Implementation
As described above, the portfolio approach used in SyRIN allows for the easy incorporation
of various entities and sectors into the analysis. Each entity or sector in the portfolio is
characterized by a marginal density, which is an integral part of the multivariate density that
represents the system. SyRIN requires the following steps for implementation:

Key inputs for implementation are the determination of the asset sizes, recovery rates,
and PoD of the financial intermediaries and sectors to be included in the analysis. We
discuss below important aspects related to aggregation and probabilities of distress.

The PoDs are used as an input in the CIMDO approach to infer the FSMD, which is the
multivariate density describing the interconnectedness structures across the entities and
sectors in the system.

Two different types of measures are obtained from the FSMD (Figure 8): the financial
stability measures and the loss metrics. The first are estimated by “sliding” the FSM into
different joint and conditional probabilities. The latter are obtained by Monte Carlo
simulations based on the FSMD. Note that since these metrics can be estimated from
various statistical moments of a common FSMD, the proposed measures are consistent
across each other.
Aggregation
We recommend inclusion, at the individual level, major banks and insurance companies, and
at the sector level, different types of investment and pension funds. This is justified by the
fact that individual banks and insurance companies usually undertake a significant proportion
of financial intermediation (measured by asset size) in financial systems. Moreover, these
entities might follow different business strategies, and hence, might be subjected to different
risk factors that should be accounted for when assessing systemic risk. Data are also a
consideration, since it is usually possible to get data at the entity level for banks and
insurance companies.
37
We adopt the following convention for certain sectors: HY = High yield bond mutual fund, IG = Investment
grade bond mutual fund, and Sov = Sovereign bond fund sector. Bond = (Sovereign + HY + IG) bond fund,
unless otherwise stated.
29
In contrast, we recommend inclusion of mutual funds, hedge funds, and pension funds at a
sector level, aggregating them by common categories. This is because performance for such
funds is usually benchmarked within their own sector; hence, their business strategies and
therefore the risk factors affecting the funds within a sector are usually common. Essentially,
this suggests that there is little benefit to be gained from incorporating fund-by-fund level
information. An example of such benchmarking is illustrated for the case of the US High
Yield sector. Figure 9 shows the consistently high correlation between the average return of
the 10 largest US High Yield Mutual Funds and ETFs (by assets under management), and the
US High Yield Index. 38 Additionally, data at the sectoral level for this these types of funds
are more readily available. Therefore, we recommend including relevant sectors, for example
HF, pension funds (PF), money market funds (MMF), EF, high yield funds (HY), Sovereign
bond funds (Sov), and investment grade bond funds (IG). However, this taxonomy can easily
be adapted to specific country circumstances and data availability.
Figure 9. Correlation of Returns: High Yield Mutual Funds and ETFs vs. the High
Yield Index
(Correlation)
1.0
0.9
0.8
0.7
0.6
2012
2013
2014
2015
Sources: Bank of America Merrill Lynch, Bloomberg L.P., EPFR Global, and IMF staff calculations.
Note: Twelve-month rolling correlation of the returns of the top 10 global high yield mutual funds as measured
by assets under management. ETF = exchange-traded fund. The average correlation over the entire period
was 0.93.
38
Another point to consider is that an analysis that incorporated thousands of funds within a sector would likely
become cumbersome.
30
Probabilities of Distress
The SyRIN is a structural risk model based on the notion of firms’ distress. PoDs can be
estimated using different models and types of data (market-based and supervisory
information). Hence, the tool can be easily adapted to cater to a high degree of institutional
granularity and data availability in different jurisdictions. The meaning of distress depends
on the type of entity and data employed. Distress events usually include default; however,
distress event effects can be broader than default and comprise, among others, debt
restructuring, government intervention, recapitalization, credit agencies’ downgrades, and so
on. An observed common feature of these distress events is that financial entities’ asset
values decrease significantly.
PoDs for banks and insurance companies. These can be estimated using market-based
information and supervisory information.

Market-based information: The most common models are the following:
o Merton type. In this case, distress is equivalent to default, as articulated in Merton’s
model (1974), which focuses on the capability of a bank to service its debt
obligations, that is, credit risk.
o CDS spreads. PoDs can be estimated using credit default swap (CDS) spreads. In
these cases, distress is defined by the event that triggers the payment of a CDS.
o Bond spreads. PoDs can be estimated using the no-arbitrage theorem
since the yield of a bond that is subject to credit risk is a function of the probability of
default (in this case distress refers to default of the bond).

Supervisory information: PoDs can be constructed from supervisory information when
market-based data is not available or not adequate. For example, in countries where
subsidiaries of foreign banks operate, it might not be possible to get market-based
indicators on the subsidiary (such indicators might exist for the consolidated bank, but
this is not adequate). In these cases, we have employed supervisory information to
estimate a bank’s PoD, which indicates the probability that losses experienced by a bank
would violate a supervisory-defined capital buffer (Segoviano and Padilla 2006). 39
Probabilities of distress for investment funds. Based on the discussion presented in
Section II, we define the PoD for investment funds as the probability of events that would
require funds to liquidate assets to meet redemption demands. Thus, when funds experience
39
Risk parameters of banks’ loan portfolios (loans’ probabilities of default, exposures, and loss-given default)
are used to estimate banks’ loss distributions (PLD). Supervisory information is used to define thresholds of
capital buffers that, if violated, would indicate a distress event; for example, supervisory intervention. PLDs and
thresholds are then used to estimate the banks’ PoD; that is, the probability of violating the supervisory
threshold.
31
strong outflows, they are likely to sell their assets to meet such demands, transmitting shocks
to other financial entities in a system via the direct exposure and asset liquidation channels
(Figure 1). In order to estimate such probabilities, we follow a Value at Risk (VaR) approach.
With this approach, it is necessary to get asset returns information for the types of funds
under analysis (taking account of aggregation aspects discussed above). With the distribution
of asset returns for each type of fund, a threshold related to periods of significant outflows is
defined. Once such a threshold is defined, it is possible to define the PoD as the probability
that returns would be lower than the level indicated by the threshold (see Appendix 1 for
technical details).
Probabilities of distress should be consistent with differences in risk factors faced by
different types of financial entities. Business models of different financial intermediaries
usually expose those entities to different levels of leverage, liquidity, and maturity
mismatches. Therefore, PoDs should be consistent with differences in risk factors. Hence,
while it is not always possible to explicitly identify such risk factors, PoD measures should
be expected to be higher for entities with high leverage, liquidity, and/or maturity
mismatches. Figure 10 shows that PoDs for different types of funds estimated under the
described approach are consistent with differences across business lines.
Figure 10. PoDs Funds
0.12
0.1
0.08
0.06
0.04
0.02
0
1/1/2 007
1/1/2008
1/1/2 009
1/1/2 010
Bond Fund s
1/1/2 011
MM Fs
1/1/2 012
1/1/2 013
1/1/2 014
Hed ge Fund s
Source: Author’s calculations.
Note: As described in Section II, hedge funds’ ability to invest in derivatives, engage in buying on margin,
short-selling, and using leverage, as well as invest in illiquid assets, makes them potentially more vulnerable to
downward asset price spirals than traditional mutual funds. These vulnerabilities are reflected in hedge funds’
higher PoDs (versus MMFs and bond funds), which became significantly higher in periods of distress. Openend funds, especially bond funds that invest in less liquid assets (for example, high yield), showed larger PoDs
than MMFs.
32
B. Tool for Comprehensive Analysis
How is systemic risk evolving?

The level of systemic risk at the time of analysis was contained. As is evident from two
measures of systemic risk—the Systemic Risk Index (Figure 11) and the FSI Index
(Figure 12)—the level of systemic risk is currently lower than it was historically around
the peaks of the financial crisis and the European sovereign crisis. These two measures
reveal similar readings (in broad terms) on the evolution of, and current state of, systemic
risk.
Figure 11. Systemic Risk Index
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Q1 2007
Q1 2008
Q1 2009
Q1 2010
Q1 2011
Q1 2012
Q1 2013
Source: Authors’ calculations.
Note: This measure displays peaks at well-documented systemic episodes: Lehman collapse (September 2008);
initial stages of the European sovereign debt crisis (May–June 2010); and subsequent intensification of the
crisis after spreading to Italy and Spain
Figure 12. Financial Stability Index
5
4
3
2
1
0
1/1/2007
12/24/2007
12/15/2008
12/7/2009
11/29/2010
11/21/2011
11/12/2012
11/4/2013
Source: Authors’ calculations
Note: This measure displays peaks at well-documented systemic episodes: Lehman collapse (September 2008);
initial stages of the European sovereign debt crisis (May–June 2010); and subsequent intensification of the
crisis after spreading to Italy and Spain.
33
What are the institutions/sectors that contribute most to systemic risk?

The banking sector has the highest systemic impact in the US, followed by the insurance
sector and pension funds. Systemic risk contribution is assessed in two dimensions:
interlinkages and size. As of 2013Q4, these three sectors’ marginal contributions to
systemic risk (MCSR) amounted to 73 percent, with 32 percent for banks, 25 percent for
insurance sector, and 16 percent for pension funds (Figure 13). It is also the case that for
certain sectors—such as HY and IG—their interconnectedness measure dominates their
relative size in the financial system; as reflected in a Ratio (= MCSR/Size) greater than
unity.
Figure 13. Marginal Contribution to Systemic Risk as of 2013Q4
35%
4.5
4
30%
3.5
25%
3
20%
2.5
15%
2
1.5
10%
1
5%
0.5
0%
0
Banks
Insurance
Pension
Systemic risk
Size
Equity
MMFs
IG
Ratio (Greater than 1.0)
HY
Hedge
funds
Sov
Ratio (Less than 1.0)
Source: Authors’ calculations.
Note: Ratio is measured on the right axis.
We proceed to track the MCSR measured in absolute terms over time. This can be done for
each of the considered sectors. The increase in absolute MCSR relative to 2007Q1 is reported
in Figure 14. Findings using this measure further reiterate the increasing trend of
interconnectedness of HY funds. As of 2013Q4, this sector had witnessed the steepest
increase in MCSR relative to the base period.
How vulnerable are specific institutions/sectors to distress in other institutions or sectors?

Contributions to distress vulnerability of the banking sector from the hedge fund and
insurance sectors tend to increase prior to periods heightened distress. Contributions from
the insurance sector have been steadily increasing over time. Vulnerability to distress
brought about by distress dependence can come as a result of direct linkages (due to cross
exposures) or of indirect linkages (due to exposures to common factors), or both. Figure
15 plots the evolution of percentage contributions of different entities to banking system
distress vulnerability along with well-documented periods of elevated distress.
34
Figure 14. Increase in MCSR
Index: 2007Q1 = 1
9
8
7
6
5
4
3
2
1
0
Source: Authors’ calculations.
Figure 15. Contributions to Distress Vulnerability of the Banking Sector
Time series (in percent)
Insurance
Equity
Sov
HY bond
IG Bond
Pension
MMFs
Hedge funds
100
90
80
70
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
9
10
11
Source: Authors’ calculations.
Notes: [1] August 9, 2007: BNP halts redemptions, [2] March 14, 2008: Bear Sterns collapse, [3] July 15, 2008:
Short selling ban on GSE credit line, [4] September 15, 2008: Lehman collapse, [5] December 12, 2008: NBER
announces US recession, [6] March 15, 2009: Fed announces Treasuries purchases, [7] May 2, 2010: Greek
package, [8] November 21, 2010: Ireland package, [9] April 6, 2011: Portugal package, [10] July 10, 2011:
ECB buys Italian and Spanish bonds, [11] July 26, 2011: Draghi’s “Whatever It Takes” speech.
35
The analysis revealed that by 1Q2014, the HY sector was the most important sector for
banking sector distress vulnerability, followed by insurance and hedge funds. This finding is
based on a comparison of percentage contributions to banking sector distress vulnerability
from each of the entities at the final point of the given time span. HY sector dominates,
followed closely by insurance (Figure 16).
Figure 16. Contributions to Banking Sector Distress Vulnerability as of 1Q2014
(in percent)
30
25
20
15
10
5
0
HY
Insurance
Hedge
funds
IG
Pension
Sov
Equity
MMFs
Source: Authors’ calculations.
Contributions to insurance sector vulnerability to distress from the banking sector have
shown a steady increase over time. Importantly, the evolution of this contribution closely
tracks the evolution of contribution to distress vulnerability of the banking sector from the
insurance sector. In contrast, hedge fund sector contribution to insurance sector distress is
much less cyclical and appears fairly stable since early 2011.
The analysis reveals the banking sector to be the most important contributor to insurance
sector distress vulnerability, followed by HY and HF. For the case of the insurance sector,
comparing contributions from each of the entities at the final point of the time span
considered (that is, 3/24/2014) indicates that the banking sector’s contribution dominates,
followed by HY and HF sectors (Figure 17).
36
Figure 17. Contributions to Insurance Sector Distress Vulnerability
(in percent)
35
30
25
20
15
10
5
0
Bank
HY
Hedge
Funds
IG
Pension
Sov
Equity
MMFs
Source: Authors’ calculations.
Linkages between the banking, insurance, and bond mutual fund sectors have increased in
recent years. The analysis documents increasing interconnectedness of mutual funds and
other sectors, for example, banking and insurance. Therefore, any potential distress in, for
example, the bond fund sector, will have a greater impact on these sectors—through both
direct (balance sheet) exposures to these markets as well as through indirect exposures, for
example, through second order intermediation channels and mark-to-market exposures. In
order to illustrate broad trends, between January 2008 and March 2014 the percentage
contribution by bond40 mutual funds to the vulnerability to distress of the banking and
insurance sectors has increased (Figure 18). The upward trend in this particular sector’s
contribution is steeper for banking sector distress vulnerability, as compared to the insurance
sector. We find that the contributions to vulnerability to distress of the banking and insurance
sectors to each other have increased significantly. This is consistent with the evidence of
convergence on business models of these sectors, that is, the insurance sector becoming more
“bank-like” over time.
40
‘This refers to the aggregate of Sov, HY and IG bond mutual fund sectors.
37
Figure 18. Contributions to Distress Vulnerability of the Banking (left) and Insurance
(right) Sectors as at January 2008 and March 2014 (in percent)
100
100
Pensions
80
Insurance
60
Hedge funds
40
Equity
20
Bond
0
Jan-08
Mar-14
Pensions
80
Banks
60
Hedge funds
40
Equity
Bond
20
0
Jan-08
Mar-14
Source: Authors’ calculations.
How does the level of systemic risk relate to the distress vulnerability?

While the level of aggregate systemic risk was contained during the period covered by
the analysis, the contributions to distress vulnerability from certain sectors had increased.
In assessing measures of systemic risk level in conjunction with distress vulnerability, it
is shown that, for the case of the banking (Figure 19) and insurance (Figure 20) sectors,
since early 2012, and in spite of aggregate systemic risk falling to a low level
(comparable to its level around 2007Q1), vulnerability to distress from shocks from
certain sectors has increased sizably.

Specifically, contributions to banking sector distress vulnerability from insurance and HY
sectors had been trending upward over most of the sample, and have increased fairly
steeply since early 2012. This corresponds to the same period where the level of systemic
risk has been witnessing a steady decline. We also see that the increase in systemic risk
level is typically preceded by a buildup of distress vulnerability of the banking sector to
shocks from the hedge fund sector. Considering results for the insurance sector, we find
that contribution to insurance sector distress vulnerability from the banking sector has
exhibited a general upward trend over the sample. This is consistent with the observation
of the convergence in business models of banking and insurance sectors.

Furthermore, we note that, whereas there is an accumulation of the hedge fund sector’s
contribution to banking sector distress prior to a rise in the level of overall systemic risk,
this leading relationship of the hedge fund sector vis-à-vis the insurance sector is far less
compelling. The contribution of the hedge fund sector to the insurance sector distress is
generally stable (see figures 19 and 20), while in contrast, its impact on banking sector
distress is characterized by relatively more pronounced cyclicality.
38
Figure 19. Comparison of Systemic Risk Level with Contribution to Banking Sector
Distress Vulnerability
50
1
45
0.9
40
0.8
35
0.7
30
0.6
25
0.5
20
0.4
15
0.3
10
0.2
5
0.1
0
2007
0
2008
2009
Hedge funds
2010
Insurance
2011
HY
2012
Pension
2013
2014
Aggregate Systemic Risk Index (rhs)
Source: Authors’ calculations.
Note: LHS axis denotes percent.
Figure 20. Comparing Aggregate Systemic Risk level with Contribution to Insurance
Sector Distress Vulnerability
50
1
0.9
40
0.8
0.7
30
0.6
0.5
20
0.4
0.3
10
0.2
0.1
0
2007
0
2008
2009
2010
2011
Bank
HY
Pension
Hedge Funds
Source: Authors’ calculations.
Note: LHS axis denotes percent.
2012
2013
2014
Aggregate Systemic Risk Index (rhs)
39
C. SyRIN: A Tool for Guiding Deeper Analysis, and Related Policy Recommendations
In this paper, we have shown SyRIN to be a useful tool for analyzing problematic sectors.
For example, based on publicly available data, the tool identified the high yield mutual fund
sector as having potentially high systemic risk impact. Key conclusions of the previous
section are:

The level of interconnectedness of HY sector relative to size (that is, ratio) exceeds unity and
has been on an upward trajectory since early 2012. This ratio currently stands at close to
its level leading up to the financial crisis.

The vulnerability to distress of the banking and insurance sectors to shocks from the HY
sector has been increasing dramatically in recent years.
Focus on the HY sector revealed the underlying structural reasons responsible for the trends
uncovered:

41
Increasing flows into mutual fund and ETF investments, particularly into riskier, less
liquid asset classes such as high yield, have taken place. In response to the crisis,
authorities took bold and coordinated action that helped mitigate what could have
potentially been another global depression. However, there is a tension between the
following two types of policies pursued during the recovery. On the one hand, monetary
authorities have deliberately sought to stimulate demand though near-zero short-term
interest rates and unconventional monetary policy, removing low-risk, longer-duration
assets from the market (Figure 21) and impelling investors to reallocate into riskier asset
classes. At the same time, U.S. (and also European) regulatory authorities took steps to
make banks safer by raising regulatory capital standards and drawing stricter limits on
trading activities. By simultaneously pursuing unconventional accommodative monetary
policies and tightening regulatory and capital standards on banks, authorities have
spurred an appetite for risk that cannot be fully met by the regulated bank sector. This
has contributed to the nonbank financial sector becoming a substantial holder of risk,
with increasing ownership of corporate and riskier foreign debt. This has happened at a
time when the average holdings of these securities by market makers (that is, brokerdealers) have decreased sharply. This growth has been particularly strong in the less
liquid fixed income markets, such as high yield,41 as low rates prevailing during the
period of quantitative easing have also led to an increased supply of bonds by riskier
companies.
In addition to leveraged loans and emerging markets debt.
40
Figure 21. Accommodative Monetary Policies Have Encouraged Greater
Risk-Taking
Central bank balance sheets have expanded
globally…
… pushing down interest rates and
stimulating risk appetite…
Change in Total Assets Outstanding
($tn, 2007=0)
Central Bank Balance Sheet as % of GDP
3
20
5
0
0.5
0
0
2008 2009 2010 2011 2012 2013 2014 2015 2016
…with mutual funds and ETFs having an
increasing share of risky assets…
20
18
Share of Total US Corporate and Foreign Bonds
(%)
… particularly in the less liquid AE credit
and EM fixed income markets.
5
Mutual Funds and ETFs (LHS)
1.2
4
1
14
3
0.8
12
2
0.6
16
10
8
6
2016
10
1
2015
40
15
2014
20
1.5
2013
60
2012
25
2
2011
80
30
2010
35
Corporate and Foreign
Bonds
2.5
2009
100
2008
40
ECB (LHS)
BoJ (RHS)
2007
BoE (LHS)
Fed (LHS)
1
0
Mutual Fund and ETF Assets under
Management ($ tn)
US High Yield
US Bank Loans
0.4
0.2
0
2007200820092010201120122013201420152016
Sources: Bloomberg, Federal Reserve, EPFR Global, and Authors’ calculations.

HY mutual funds face rising redemption risks. While institutional investors, such as
pension funds and insurance companies, have relatively stable funding structures, mutual
funds and ETFs are vulnerable to investor withdrawals. The liquidity provided by funds
can quickly reverse, with fund inflows historically reversing during times of stress,
particularly for funds that invest in riskier, less liquid, fixed-income products. This risk is
compounded by the fact that mutual funds and ETF vehicles engage in liquidity
transformation, offering demandable equity in substantially less liquid underlying
investments, and therefore may make the liquidation of a significant amount of their
41
holdings very problematic within short periods of time, particularly during episodes of
turmoil.42

Redemption risks increase, due to mutual fund growth and declining inventories in the
less liquid markets, as represented by the number of days it will take to fully liquidate all
holdings should all investors decide to redeem at once (Figure 22). This is a concern as,
for example, the current SEC regulations only permit funds to delay paying redemptions
to their investors for a maximum of seven days. 43
Figure 22. Days Required for Full Liquidation
Days
200
180
160
140
120
100
80
60
40
20
7-day limit for redemption payments
0
2007
2008
2009
2010
2012
2013
2014
2015
Source: EPFR Global, Federal Reserve, and authors’ calculations.
Note: The number of days to liquidate is the ratio of assets of US high-yield mutual funds and ETFs per daily
dealer inventories. Under the Investment Company Act of 1940, US open-end mutual funds may not postpone
the payment of redemption proceeds for more than seven days following receipt of a redemption request.
Because there are no data for US high-yield bond-dealer inventories before April 2013, the dashed red line
assumes a constant ratio of this amount to total corporate bonds before this date.
42
Most mutual funds in the less liquid fixed income markets offer the promise of daily liquidity to their
investors, while ETFs offer continuous intraday liquidity given that they trade at exchanges.
43
Under section 23(e) of the Investment Company Act of 1940 Provisions: “Open-end funds may not suspend
the right of redemption, and open-end funds may not postpone the payment of redemption proceeds for more
than seven days following receipt of a redemption request.” Under exceptional circumstances, mutual funds
may be allowed to suspend redemptions temporarily should (i) the disposal of securities by a mutual fund is not
“reasonably practicable” or (ii) it is not reasonably practicable for such fund fairly to determine its NAV. In
theory, there is a mechanism in which the SEC has the authority to authorize asset managers to suspend when
facing large redemptions. See, for example, the case of the Third Avenue Focused Credit Fund, which
announced the suspension of redemptions on December 9, 2015, and blocked future investor redemptions
following a period of large losses and investor outflows. However, it remains to be seen how effective this
mechanism would be on a large scale, as it has never been tested before in a period of significant distress across
financial markets.
42
The observed growth in mutual fund and ETF holdings means that they have become key
players in credit intermediation, and are, therefore, becoming increasingly interconnected
with the rest of the financial system. For instance, the increase in the percentage
contributions of HY mutual funds to the vulnerability of the banking and the insurance
sectors reflects the fact that market and liquidity pressures in mutual fund holdings of HY
bonds may negatively impact the banking and insurance sectors, both directly through
balance sheet exposures as well as indirectly through common mark-to-market exposures and
contagion through the asset liquidation channel.
Policy recommendations to mitigate these risks have been discussed in recent IMF Global
Financial Stability Reports.44 To address the contagion risk stemming from the liquidity
mismatch posed by high yield mutual funds, policy should consider measures to limit the
incentives for mutual fund investors to run, enhancing the accuracy of NAV calculations, and
improving the liquidity and transparency of secondary markets. Specifically:

Regulators should consider a tailored approach when assessing the relative liquidity of
specific asset classes compared to the redemption terms offered by comingled investment
vehicles such as mutual funds. For example, in markets with frequently observed
transactions and substantial depth, such as advanced economy money markets and
sovereign debt, the current practice of striking a daily NAV and redemption terms may be
appropriate. In less frequently traded markets in which bid-ask spreads can be large, such
as HY bonds, lower frequency redemption terms are more appropriate. In this regard, the
seven-day maximum limit to pay for redemptions in US mutual funds (under the
Investment Company Act of 1940, Section 22e) may not be enough during stress periods,
given the existing liquidity mismatches stemming from the rise in mutual fund assets
invested in the less liquid fixed-income markets and the low level of dealer inventories in
these markets.

While the oversight of liquidity risk management has intensified and new rules have been
adopted,45 liquidity mismatch vulnerabilities remain prevalent in certain investment
vehicles and warrant further improvements to mitigate the risk. Recent initiatives such as
the IOSCO consultation on liquidity risk management recommendations (July 2017) and
the UK FCA’s discussion paper on possible approaches to improve liquidity risk
management (February 2017) have suggested possible methods to address these
vulnerabilities. There is room for improvement in different areas including greater
44
45
See Chapter 1 of the October 2014 and May 2015 IMF’s Global Financial Stability reports.
The SEC adopted a new liquidity risk management rules in October 2016 and the FSB published a series of
policy recommendations to address structural vulnerabilities associated with asset management (including
liquidity risk management) in January 2017.
43
flexibility in redemptions and dealing frequencies 46, the treatment of institutional
investors, better guidance on the use of specific risk management tools and enhanced
disclosure requirements.

Improving the accuracy of NAV calculations should also reduce stability risks associated
with HY mutual funds. The SEC rules for US mutual funds state (under the Investment
Company Act of 1940, Rule 22c-1) that “an open-end fund generally must compute its
NAV at least once daily, Monday through Friday.” This may not be appropriate for funds
invested in the less liquid assets, where the computation of a daily NAV often relies upon
third-party “matrix-pricing” services that use algorithms and assumptions to generate
estimates of fair value. Analysis in the GFSR shows how only a quarter of the bonds in
the Barclays High Yield Index trade every day, and therefore setting a daily NAV for
these securities poses challenges. The necessary use of matrix pricing can be misleading
(See pp. 33 of Chapter 1 Oct 14 GFSR). This can lead to large price drops during volatile
markets, and potentially to further redemptions from end-investors unaware of the limited
liquidity of the underlying investments. Therefore, where transactions are infrequent, it is
recommended that mutual funds change to less frequent NAV pricings, in line with lower
frequency redemption terms that better match the liquidity of the underlying investments.

Regarding ETFs, the regulatory authorities, which can withhold permission from
particular fund types, should make the liquidity—or illiquidity—of the underlying assets
a major criterion in the approval process of new ETFs.

Insurance products which incorporate significant elements of protection against market
moves should only be allowed if they are fully hedged with derivatives traded on
exchanges or trading platforms. Insurance products that are subject to adverse moves in
single factors (mortgage or bond insurance) should also only be provided if hedged with
exchange- or platform-traded derivatives.

Greater emphasis should be placed on asset managers’ communication with investors
about the risks inherent in mutual funds invested in certain markets that may be subject to
greater liquidity risks and volatility, particularly during periods of distress.

Finally, given the complexity of these issues, it is crucial that regulators pursue a
harmonized and coordinated global effort to examine the universe of mutual funds when
considering prudential policies, and to develop best practices for addressing redemption
risks as well as the supervision of liquidity and pricing of illiquid securities.
46
Greater flexibility in redemption and dealing frequency under the European Union’s UCITS Directive is a
step in the right direction. The directive allows funds to have redemption frequencies of up to twice a month,
which may help minimize the risk of liquidity mismatches. However, only a small proportion of funds invested
in illiquid assets, such as high yield bond funds, offer redemption terms at a lower than daily frequency under
UCITS, which has been related, amongst other reasons, to the inability of fund distribution platforms to
accommodate any other fund dealing pattern than daily.
44
Going forward, SyRIN can be of use in assessing the impact of ongoing structural changes in
financial markets. Hence, we hope that SyRIN represents a useful tool in the process of
policy formulation, with the objective of minimizing vulnerabilities due to:

Increased sensitivity of capital markets to the exit from unconventional monetary
policies. The vulnerability of markets to asset price movements and the increased crossmarket interconnectedness experience in recent years could make the exit from
unconventional monetary policies more volatile, potentially undermining the ability of
the financial system to support the recovery.

The rise of passive investment and financial market participants becoming increasingly
marked-to-market and benchmark-centric. This contributes to increases in sensitivity to
changes in asset prices and the interconnectedness between them. There has been a
substantial increase in the share of risky assets held by passive investment vehicles such
as index funds and mutual funds.47 For example, the share of total U.S. public equities
held by index funds and ETFs rose from 6 percent in 2007 to 16 percent by end-2016
(Figure 23). At the same time, there is evidence of increased procyclicality of pension
funds and insurance companies at a time of significant reduction in bank dealer market
making activities.48

Changing cross-country financial interlinkages. The globalization of financial services
has increased financial interconnectedness, not only across sectors but also across
borders.

Higher vulnerabilities for emerging markets to shocks originating in advanced
economies. Financial links between advanced markets and emerging markets are now
stronger, reflecting portfolio concentration and the changing nature of asset price
volatility, exposing emerging markets to shocks emanating from advanced economies.
47
There is a growing body of academic work warning about the risks related to the growth of passive investing.
Wurgler (2011) argues that the increase in passive investment inhibits the ability of active managers to beat
benchmarks and can also lead to greater risk of asset price bubbles followed by crashes as it may encourage
trading activity that exacerbates those risks. Wermers and Yao (2010) find that stocks with “excessive” levels of
passive fund ownership exhibit more long-term pricing anomalies as well as a larger price reversal following
trades. Sullivan and Xiong (2012) also find that the growing popularity of passive investing contributes to
higher systemic market risk.
48
See Bank of England and the Procyclicality Working Group, 2014.
45
Figure 23. The Rise of Passive Investment in US Equity Markets
Sources: Federal Reserve, ICI, and authors’ calculations.
V. CONCLUSIONS
Systemic risk can be amplified across various financial intermediaries and markets.
Therefore, a proper measurement of systemic risk requires an assessment of risk beyond the
banking system that also accounts for interconnectedness across financial entities and
markets which could pave the road for financial contagion. SyRIN allows a comprehensive
assessment of systemic risk by quantifying the impact of systemic risk amplification
mechanisms due to interconnectedness structures across banks and other financial
intermediaries, including the insurance, pension fund, hedge fund, and investment fund
sectors—risk that cannot be captured when analyzing sectors independently. The tool
produces various metrics that serve to evaluate systemic risk from complementary
perspectives, including tail risk, interconnectedness across different entities, and the
contribution to systemic risk by multiple entities and sectors.
SyRIN is easily implementable with publicly available data and can be straightforwardly
adapted to cater to different degrees of institutional granularity and data availability. SyRIN
is designed to be a tool to identify vulnerabilities from a top-down perspective that can lead
to deeper scrutiny in specific sectors and that can provide information to authorities to assist
their policy formulation process.
SyRIN’s contributions are increasingly relevant, as structural changes in financial
intermediation are shifting the locus of risk to the non-financial sector, and involve
increasing interlinkages across sectors. Going forward, SyRIN can be useful for assessing the
impact of emerging vulnerabilities due to increased sensitivity of financial markets to the exit
of unconventional monetary policies, higher vulnerabilities of EMs to shocks originating in
AEs, and increased procyclicality arising from structural changes in markets.
46
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50
APPENDIX I. INPUTS FOR IMPLEMENTATION
The implementation of SyRIN requires as inputs PoDs and total assets of the entities and
sectors under analysis. Our dataset includes PoDs at the individual entity level (banks and
insurance companies) and sectors (mutual funds, pension funds, and hedge funds). This
appendix describes the method employed to estimate the PoDs for each of those entities and
sectors.
PoDs: Banks and Insurance Companies
For banks and insurance companies in the sample, PoDs are estimated using CDS spreads 49,
following Segoviano and Goodhart (2009). Based on the no-arbitrage theorem and assuming
a recovery rate (R) of 40 percent, PoDs are defined by the following formula:
PoD t 
CDS t
.
1 R
The entities included in the banking and insurance sectors are as follows:

Banking: Bank of America, Capital One Financial, Citigroup, Goldman Sachs,
JPMorgan, Morgan Stanley, and Wells Fargo. These entities cover approximately
67 percent of total US banks’ assets.

Insurance: AIG, Allstate, Berkshire Hathaway, Hartford Financial Services, MetLife,
Prudential, and Travelers Companies. These entities cover approximately 50 percent of
total U.S. insurance sector assets.
PoDs: Investment Funds
We follow a VaR approach. As discussed in Section IV.A, this approach requires an
estimation of the distribution of asset returns for each type of fund under analysis, and a
threshold related to periods of significant outflows. Once such a threshold is defined, it is
possible to define the PoD as the probability that returns would be lower than the level
indicated by the threshold. The steps in this estimation were the following:
1. Compile daily stock price: Pt
2. Compute daily stock price return: rt = log(Pt ) – log(Pt-1)
3. Standardize rt by subtracting full sample mean ̅ and dividing by full sample standard
deviation σ.
̅
4. Standardized returns are thus given by:
=
5. Compute distress threshold for the full-time series such that 1 percent of returns fall
below this threshold. This is analogous to 99 percent VaR and is consistent with periods
of high outflows.
49
We use five-year CDS spreads from CMA retrieved through Datastream (or Bloomberg).
51


Re-order series in ascending order—lowest to highest; i.e.
Delineate the 1st percentile of , series. Call this γ.
,
.
6. Compute time series of PoDs.


Corresponding to each point in time —compute rolling sample mean and standard
deviation for six months prior (trailing window).
Using these sample moments for and assuming a Normal distribution—at each
point in time compute the probability that returns fall below γ.
Returns can be estimated using mark-to-market asset value data, noting that, depending on
how data are presented, the estimation of returns might need to be adjusted by redemptions
and subscriptions to ensure that returns reflect adequately the impact of price changes.
In cases when mark-to-market asset value data are not available, it is possible to proxy markto-market data by reconstructing funds’ asset portfolios and estimate their market value based
on individual asset prices. Input series for this procedure are described in Table Appendix
Table 1.
Appendix Table 1. Input Series for Portfolio Reconstruction
Equity funds
75% MSCI US index, 25% MSCI World index. Weights derived from ICI data.
Ticker
MSUSAM$ (US), MSWRLD$ (World)
Field
MSRI
Bond funds
(incl. HY and IG)
BofA Merrill Lynch US Corporate & Government Index, BofA Merrill Lynch US High Yield
Index, and BofA Merrill Lynch US Corporate Index (Investment Grade)
Ticker
B0A0, C0A0 (Investment Grade), and H0A0 (High Yield)
Field
ML: RIUSD (Price), ML: OAS (Spread)
MMF
Ticker
40% 3M Certificate of Deposits, 20% O/N repo, 20% 3M Commercial Paper, 10% Asset-Backed
Commercial Paper, and 10% T-Bills
FRCDW3M (CDs), USORGCP (O/N repo), FRCPN3M (CP), USCPA3M (ABCP), and
FRTBS3M (T-Bills)
Field
Price (Yield)
Pension funds
50% domestic equities (MSCI US index), 25 % foreign equities (MSCI World index), and 25%
bonds (BofA Merrill Lynch US Corporate & Government Index). Weights derived from ICI data
[1].
Ticker
MSUSAM$ (US), MSWRLD$ (World), and B0A0
Fields
MSRI, ML: RIUSD (Price), ML: OAS (Spread)
Hedge funds
HFR index
Ticker
HFRXHF$
Field
RI
52
Data on Total Assets per Sector
We employ bank and insurance sector data from the US Flow of Funds (FoF).
Data by fund type, that is, equity, bond funds, and MMFs, come from the ICI.
The size of each fund is estimated by applying the relative weights derived from ICI to the
FoF data.
Data are retrieved using Datastream or Bloomberg at a quarterly frequency. When data are
annual, a linear interpolation to obtain the quarterly data is used.
For HF we use Barclayhedge data50 on global HF and apply a 75 percent weight for the US,
where the weight is derived from the 2013 IOSCO survey. 51 Total asset data sources are
provided in Appendix Table 2.
Appendix Table 2. Input Series for Total Asset Data
Banks
Private depository institutions (Table L.109)
Ticker
US70PDTAA
Insurance
Life insurance (L.115) + other insurance Corporations (L.114)
Ticker
US54XXXAA, US51XXXAA
Equity funds
ICI—all equity funds
Ticker
USFANEQ.A
Bond funds
ICI—All bond and income funds TNA [2].
(Incl. HY and IG)
IG and HY bond funds are a subset of bond funds (that is, three subsectors: Investment Grade,
High Yield, and Sovereign). Their relative shares are derived using ICI yearly figures. [3]
Ticker
USFANBI.A
MMF
ICI MMFs TNA
Ticker
USFANMM.A
Pension funds
Private and public pension funds (L.116)
Ticker
US59TOFAA
Hedge funds
AuM of hedge funds—weight 75%
Ticker
Barclayhedge website
Notes:
[1] http://www.ici.org/research/stats/retirement
[2] TNA refers to total net assets.
[3] We used Table 4, available at http://www.icifactbook.org/2013/fb_data.html#section5.
50
51
http://www.barclayhedge.com/research/indices/ghs/mum/HF_Money_Under_Management.html
http://www.iosco.org/library/pubdocs/pdf/IOSCOPD427.pdf