Oet, Mikhail with Dieter Gramlich and Stephen Ong  "Policy in Adaptive Financial Markets—The Use of Systemic Risk Early Warning Tools", 2013 July 21 - 2013 July 25

Policy in Adaptive Financial Markets—The Use of Systemic Risk Early Warning Tools.

Original version: May 25, 2012
This version: March 18, 2013
(Preliminary, Not for Quotation)

Abstract

How can a systemic risk early warning system (EWS) facilitate the financial stability work of
policymakers? In the context of evolving financial market dynamics and limitations of
microprudential policy, this study examines new directions for financial macroprudential policy.
A flexible macroprudential approach is anchored in strategic capacities of systemic risk EW Ss.
Tactically, macroprudential applications are founded on information about the level, structure,
and institutional drivers of systemic financial stress and aim to manage the financial system risk
and imbalances in two dimensions: across time and institutions. Time related EWS policy
applications are analyzed in pursuit of prevention and mitigation. EWS applications across
institutions are considered via common exposures and interconnectedness. Care must be taken in
the calibration of macroprudential applications, given their reliance on quality of the underlying
systemic risk-modeling framework.

Keywords: financial stability, regulation, macroprudential, policy instrument, early warming
system, systemic risk, financial stress, imbalance
JEL classification: G01; G18; G28; E32; E37

Contents

1. INTRODUCTION
2, RESEARCH QUESTION AND CONCEPTUAL MODEL..
3, THEORETICAL FRAMEWORK FOR MACROPRUDENTIAL TOOLS...
3.1. Objectives of Macroprudential Policy
3.2. Functions of Macroprudential Policy Tools
3.3. Forms of Macroprudential Policy Tools
3.4. Policy Evaluation in A daptive Financial System

4. USE OF SYSTEMIC RISK EARLY WARNING TOOLS FOR POLICY ......seesseeessssees
4.1. A Tour of a Systemic Risk Early Waring System
4.2. Macroprudential EWS Use for Time-Dimension Objectives ..........cesseseesesseesneeseene
4.2.1. Time phases and policy
4.2.2. EWS instruments in the time dimension

4.3. Macroprudential EWS Use for Cross-Sectional Objectives...
4.3.1. Cross-sectional directions and policy
4.3.2. EWS instruments in the cross-sectional dimension

5. CONCLUSION
REFERENCES
TABLES
FIGURES


1, INTRODUCTION

The shared limitations of risk managers and regulators to abate the persistent spillovers in
the financial systems also challenge the development of early waming tools that help us spot
possible instabilities. This study aims to consider potential policy applications of such systemic
risk early warning systems in the context of these limitations.

The recent financial storms seem to have caught most of us by surprise. Large diversified
financial institutions with massive investments in risk management leamed they knew virtually
nothing about systemic risk and correlated failure. At the same time, central banks leamed that
despite their focus on early warning models of individual institutions’ safety, these models were
not able to anticipate the coming crisis. The international differences in the intensity of
supervisory spectrum—from Fed’s regulation-intensive to BOEF’s regulation-light'—made little
difference: in this joint crisis of the financial systems, financial institutions and central banks
were similarly caught unawares.”

In the post-mortem of the 2007-2009 global financial crisis, the harsh judgments on
institutional failures have been universal. Risk management failures were highlighted through a
litany of crisis-related problems including: creation of incentives and lack of controls for rogue
trading, “price fixing” (e.g. LIBOR), foreclosure abuses, international “money laundering”, “tax
evasion,” and “misleading clients with worthless securities” while profiting by them with
offsetting bets (Denning, 2013). Hellwig (2009, p. 51) criticized “risk managers, risk controllers,
and, most importantly, top management at institutions... for not having taken account of the
possibility of ... [systemic risks]. They relied on the quantitative risk models that they had
developed and believed in their ability to control risks on the basis of these models. Their
exposure to systemic risks ... had not been incorporated into the models — and could not have
been incorporated because they did not have the requisite information.” Similarly, Haldane and
May (2011, p. 351) attributed risk management flaws to the poor understanding of the financial
system complexity including the systemic spillover potential that came with the extraordinary
innovation in financial instruments: “In the run-up to the recent financial crisis, an increasingly
elaborate set of financial instruments emerged, intended to optimize retums to individual
institutions with seemingly minimal risk. Essentially no attention was given to their possible
effects on the stability of the system as a whole.” Stulz (2008) argued that the critical failures
warrant improvement in risk management, particularly improvement focused on scenarios
involving the spillover and feedback mechanisms involved in crises.

Regulatory shortcomings were equally exposed. For example, the recently released
transcripts of the deliberations of the Federal Reserve Federal Open Market Committee (FOMC)?
during 2007 revealed that “Federal Reserve officials were largely 1 unaware of the financial crisis
brewing in 2007, until they found themselves in the middle of it.” The European Central Bank
had been similarly criticized for turning “‘a blind eye to “irresponsible lending” by German,
French, British and Belgian banks, and for “failing to use its powers to rein in speculative

1 Goodhart (2004), Mayes and Wood (2007), Ellyatt (2012, December 14).

2 Gordon Brown (2010) called it a failure of collective action, as global institutions jointly failed to keep pace

with the i ilities inherent in i lated global

Full transcripts of FOMC deliberations are released to the public with a five year lag.

* Kurtz, A. (2013, January 18). Federal Reserve was blind to crisis brewing in early 2007 - Jan. 18, 2013.
CNNMoney - Business, financial and personal finance news. Retrieved January ; 24, 2013, from
http://money.cnn.com/2013/01/1 ‘federal-res ipts/index.html

3


bubbles in countries such as Ireland and Spain” (Phillips, 2011). Richter and Wahl (2001, p. 4)
conclude that “the ECB did not see the crisis coming.” This case of blindness had to have been
very contagious, as few central bankers were immune. Bank of England’s failed to see the crisis
coming as well, according to its deputy Govemor Sir John Gieve who urged the development of
tools bridging the interaction “of individual banks and ... financial cycle and prevent[ing] the
financial cycle... getting out of hand. 2

Some consider too much regulation to be the problem, others blame too little regulation.®
Some say that our hubris to rely on models has been exposed and any efforts to manage the
future state of risk exposures, the economy, financial systems, etc. that are based on patterns
from the past are necessarily doomed.’ These considerations emphasize the persistent limitations
of traditional, micro-focused, early waming models’ to allow risk managers and regulators to
cope with risk. In fact, “many observers? have argued the regulatory framework in place prior to
the global financial crisis was deficient because it was largely “microprudential” in nature. wll
Others emphasize that this failure of microprudential models is endemic: “ “microprudential EWS
models cannot, because of their design, provide a systemic perspective on distress.”"’ They argue
that a disruption in the financial system by its very nature manifests our collective failure to
control it.'? Where most people agree is that disruptions of financial Stability are disturbing and
create spillovers that must be handled—a problem of systemic risk.'* Thus, risk managers,

Winnett and Swaine (2008, December 22). This extraordinarily impaired sight appears in no-way a unique
attribute of the recent turmoil. Recall, for example, the staggering optimism of the Japanese asset prices in the
80’s. “At the peak of the speculative bubble in 1989, the gardens of the emperor’s palace were worth more than
all of Canada. A 1200 square meter premise in central Tokyo was worth almost 850 million US-dollars
(Hanusch and Wackermann, 2009, p. 6).” Despite the unprecedented bubble, the Bank of Japan and the
Japanese financial institutions, failed to foresee the burst of the speculative bubble in 1991 that initiated the
infamous “lost decade” crisis (Hayashi and Prescott, 2002; Hanusch and Wackermann, 2009).

See Eichengreen (2009) and Friedman and Kroug (2011) on failures to regulate. These failures would include
examples of both potential overregul (e.g. C Act) and potential underregulation
(e.g. financial liberalization and deregulation). See Paul (2008), Roberts (2008), Demyanyk and Van Hemert
(2011), Ergungor (2007) for the arguments, evidence, and analysis of the relationship of Community
Reinvestment Act with the 2007-2009 financial crisis. See Saunders et al. (2012) for analysis of deregulation
and risk taking and Crotty (2009) for their structural significance to the global financial crisis.

7 Stulz (2008).

Borio (2003) classifies regulatory early warning system (EWS) models as micro- or macroprudential.
Traditional EWS models “extrapolate the risk of a single financial institution (micro risk)” (Gramlich et al.,
2010) and aim “at preventing the costly failure of individual financial institutions (Hanson et al., 2011).

For example, see Cihak and Poghosyan (2009) who analyze limitations of traditional CAMEL grades and
suggest that market-based provide significant e Yy power not ined in the indi of individual
institutions. Hanson et al. (2011) provide an extensive review, citing Crockett (2000), Borio et al. (2001), Borio
(2003), Kashyap and Stein (2004), Kashyap et al. (2008), Brunnermeier et al. (2009), Bank of England (2009),
French et al. (2010) to support this point.

10 Hanson et al. (2011), p. 1.

1. Gramlich et al. (2010), p. 208.

Brown (2010) writes: “The crisis exposed the iction of globalization itself: as ies have become
more interconnected, regulators and governments have failed to keep pace and increase coordination (p.7). ... It
is a failure intrinsic to unregulated global markets, an instability that results from the manner in which
increasing flows of capital around the world happened and impacted the economy. And it is a failure of
collective action at an international level to respond quickly enough to structural imbalance and inequities that
arose. At its simplest, then, this is the first true crisis of globalization (jacket notes).”

“The prevalent view (Group of Ten, 2001) is that systemic financial risk is the possibility that a shock event
triggers an adverse feedback loop in financial institutions and markets, significantly affecting their ability to


regulators, and academics increasingly study factors that explain systemic risk, consider
vulnerabilities and patterns of contagion within the networks making up the financial system,
study the feedbacks mechanisms that give rise to destabilizing spillover patterns, in short, work
on the problem to advance our understanding of risk in the financial system as a whole—the
macroprudential problem. ' This work which commenced significantly after the wave of the
global financial crises in the 1990-s is beginning to bear fruit on every front: in the study of
financial networks, study of systemic feedback mechanisms, as well as the study of
macroprudential early warning systems. A new wave of understanding leaves researchers
cautiously optimistic in yielding encouraging “evidence that early warning indicators exist which
signal costly asset price developments in 'real time' and with sufficient lead to react.” °
In affirming the regulatory responsibility for financial stability!°, Chairman Bemanke

highlighted the critical question of the appropriate “field of vision”:

“Under our current system of safety-and-soundness regulation, supervisors often

focus on the financial conditions of individual institutions in isolation. An alternative

approach, which has been called systemwide or macroprudential oversight, would

broaden the mandate of regulators and supervisors to encompass consideration of

potential systemic risks and weaknesses as well. ah

At the same time, no systemwide oversight and no early warning tool of systemic risk can
get very far without a good understanding of the system. Here critically, a gigantic body of
interdisciplinary research reveals that financial system is complex: inherently unpredictable in
the long-run, sensitive to initial conditions, adaptive to change in its organizational patterns, and
revealing non-linear dynamic behavior.'® Thus, our puzzle becomes to understand the potential
policy options that emerge with the new macroprudential tools of early warning in the context of
the evolution of financial markets’ complexity.

The rest of this paper is structured as follows: Section 2 discusses the research question
and the conceptual model of financial stability tools in adaptive settings. Section 3 discusses
potential uses of the emerging early warning tools. Section 4 concludes with a discussion of
critical policy implications.

2. RESEARCH QUESTION AND CONCEPTUAL MODEL

What are the policy applications of the emerging early warning tools for systemic risk
oversight in the context of complex and adaptive financial systems?

Figure 1 shows the conceptual model guiding the research, mapped in literature. The
model suggests that macroprudential policy in adaptive financial systems is a continuous process

allocate capital and serve intermediary functions, thereby generating spillover effects into the real economy
with no clear self-healing mechanism ([A uthors Removed], 2013a, p. 792).”

Particularly, the research at the Bank of International Settlements (Borio, 2003, 2006, 2009, 2010; Borio et al.
2001, 2002a, 2002b, 2004), at the International Monetary Fund (Nier, 2011; Lim et al. 2011a, 2011b), at the
Bank of England (Haldane and May, 2011), at the European Central Bank (Alessi and Detken, 2009; Detken,
2012), and the Federal Reserve ([Authors Removed], 2013a, 2013b).

15 Alessi and Detken (2009), p. 8.

© Bemanke (2011).

"7 Bemanke (2008).

For overview of this research see Arthur (1995) and Farmer and Lo (1999), Boyer (1999) for the analysis of
heterogeneity of financial markets, and Hollingsworth et al. (2005) for socio-economic implications of financial
system’s complexity from an institutitonalist perspective.

and must be to keep up with the financial system transformation. In this process, macroprudential

policy is continuously adjusted from its objectives, through reconsideration of its functions,

through redesign of its forms, through its methodological revaluation. This is a continuous

process of policy adjustment that reflects the adaptive transformations in the financial system.
Insert Fig. 1 about here

The context of the economy as adaptive™ complex system was pioneered by Holland
(1975, 1988) in his work on adaptive nonlinear networks and was significantly extended in the
past four decades. 20 Following Holland (1988, pp. 117-118), the global economy forms an
adaptive system through the following seven features: 1) interaction of many interdependent
agents, 2) scarcity of global controls that allow competitive, as well as coordinating and shifting
agent associations, 3) multilevel hierarchical agent associations with asymmetric interactions
across levels, 4) system adaptation through a continual recombination of agent interactions as the
system accumulates experience, 5) the presence of niches exploitable by particular agent
adaptations, 6) continuous creation of niches through technological innovation, and 7)
suboptimal performance due to the continual thriving of niche interactions.

Nicolis and Prigogine (1977, p. 464) show that in adaptive systems relative instability is a
continuous state feature of the system, and that the onset of an adaptive “process is dictated by
the behavior of the fluctuations.” In fact, the main reason for the onset of an adaptive process is
that an adaptive “system is necessarily undergoing instabilities, and hence is capable of
amplifying certain disturbances including some of its own fluctuations.””! Put differently, a
continuous state of relative financial instability is an integral aspect and an integral problem of
an adaptive financial system.

The conceptual model reflects the evolving relationship between the financial system
context and its risk policy objectives, functions, forms, and evaluation. The formation of
systemic risk policy objectives is discussed through seminal contributions of Acharya (2001),
Elsinger et al. (2002), Borio (2003), Rochet (2004, 2005), and Nier (2011). Principally these
objectives include time- and cross-sectional aspects. Systemic policy functions are considered
starting with the influential contributions of Gonzalez-Hermosillo (1996), De Bandt and
Hartmann (2000), Borio (2006) and including current research. The intrinsic functions involve
identification of systemic conditions, forward-looking and forecasting capacities, identification
of systemic imbalances, differentiation of excessive exposures, and sensitivity to systemic risk
posed. Policy forms are based following the findings of Lim et al. (2011a, 2011b) and Galati and
Moessner (2012). Current typology of these forms comprises early warning systems, asset price
models, stress-testing, and microprudential feeds. The focus of this study is on the potential
policy applications of early waming tools. Policy evaluation in adaptive financial system is

‘8 Some researchers use the term self-organizing interchangeably with the term adaptive to emphasize the

emergence of coordination among agents in the process of adaptation. In this paper adaptive is preferred, as it
refers to a more general set of i i includi dinating i i
20 See Arthur (1995) and Arthur et al. (1997) for applications of adaptive network modeling to financial markets.
Brock and Hommes (1997, 1998) study financial markets as adaptive belief systems and Hommes (2001)
extends this approach to markets as nonlinear adaptive evolutionary systems. See Aghion and Howitt (1992)
and Howitt et al. (2008) for complexity-based macroeconomic models. See Farmer (2002) and Farmer et al.
(2005) for complexity-based modeling of financial markets. See Beyeler et al. (2007), Bech et al. (2010), and
Soramaki et al. (2007) for studies on topology and contagion in specific financial markets. See Farmer (1990)
and Brock and Durlauf (2001) for critical methodologies.
Nicolis and Prigogine (1977), pp. 465.


considered in light of formative contributions of Lucas (1976), Sabatier (1991), and Brock et al.
(2003). Following Brock et al. (2003), the evaluation approaches include expected loss
calculations, model uncertainty aversion, local robustness analysis, and robustness with multiple
models.

3. THEORETICAL FRAMEWORK FOR MACROPRUDENTIAL TOOLS

3.1. Objectives of Macroprudential Policy

The inherent instability of an adaptive system also grounds the system’s meaning to its
agents and frames their objectives. Financial stability can thus be viewed as an ability to control
one’s choices in an adaptive system in order to regulate preferential outcomes which would also
include the relative state of system instability. ” This is a problem of control, of ability to
regulate one’s environment—a prudential problem’*—shared with all risk managing agents. As a
result of these inherent objectives, recent prudential policy research focuses on two topics: first, a
greater understanding of the role of prudential policies as a core component of the overall
financial stability* and, second, the particular crisis-related challenges. As an example of the
latter, Cukierman (2011) explores the issues of insufficiency of microprudential approaches to
handle macrofinancial (systemic) risk, the need to reflect new dynamics of markets in stability
policy, and the need for institutions and instruments to monitor and manage systemic risk.” As
an example of the first, Borio (2003) and Nier (2011) consider the co-existence of dual
perspectives in prudential policies: microprudential and macroprudential. Microprudential policy
seeks to limit risk of failure of individual institutions, while macroprudential policy seeks to limit
costs of financial distress in terms of the macroeconomy. Among the critical differences between
the two prudential policies is the treatment of aggregate risk in the financial system. In the
microprudential perspective, aggregate risk is exogenous — independent of behavior of individual
institutions. In the macroprudential view, aggregate risk is endogenous — dependent on collective
behavior of institutions. This endogeneity leads to the fundamental challenge in the
macroprudential approach: understanding of the process by which the risk aggregates in the
system over time and across the system participants. Thus, the principal problems of
macroprudential policies deal with aggregate risk in two dimensions: first, time and second,
cross-sectionally.”°

Policy in the time dimension is concemed with aggregate risk evolution over time
relative to the financial cycle and the adverse amplification between financial system and the real
economy (procyclicality).”” Furthermore, the time dimension objectives form a dual set of long-
term and short-term goals. In the long run the objective is “to avoid macroeconomic costs linked
to financial instability,” while in the short run, the objective is to “limit financial system-wide

Schinasi (2004, p. 8) proposes a related definition of financial stability as a continuous range where “A financial
system ... is capable of facilitating (rather than impeding) the performance of an economy, and of dissipating
financial imbalances that arise endogenously or as a result of significant adverse and unanticipated events.”

In the context of this paper, the term prudential is synonymous to the term supervisory, defined plainly as an
attribute of critical watching ([A uthors Removed], 2012).

24 See for example, IMF (2011), p. 9. Also, Cukierman (2011), p. 27.

5 Cukierman (2011), p. 31.

26 IMF (2011), p. 8.

27 Borio (2003), p. 11.

distress.””° Similarly, policy in the cross-sectional dimension is concemed with a complementary
dual set of issues: common exposures and interconnections among institutions. Accordingly, the
cross-sectional macroprudential objectives include the common exposure imbalance-based goal
to limit severity of failure, common exposure imbalance-based goal to limit probability of
failure, and interconnectedness-based goal to strengthen infrastructure resilience. Any
macroprudential tool designed to support these objectives must enable corresponding strategic
capacities.

Post-crisis normative research further details these objectives and recognizes that some
macroprudential problems also integrate other financial stability policies: monetary and fiscal.
For example, within individually common exposures, Nier (2011) adds further specificity by
distinguishing the dual—severity and probability—dimensions of failure with the idea that
different macroprudential policy instruments are needed to address them. Hannoun (2010)
further includes the goals of liquidity management, leaning against financial imbalances, and
price stability as elements of monetary stability policy. ”°

In summary, current research on prudential policy shows an effective consensus that
emphasizes the necessity of the macroprudential policy in addition to the traditional
microprudential approach. The consequent questions that need to be answered concem the
functions of macroprudential tools and their resulting design and policy applications.

3.2. Functions of Macroprudential Policy Tools

Functions of macroprudential policy tools follow the consensus view expressed by Borio
(2006, p. 3413) that macroprudential policy contains two strategic dimensions: “first, improving
measurement of systemic conditions, and second focusing on limiting build-up of imbalances.”

Identification of systemic conditions—stress identification is the basis of an effective
macroprudential policy tool. A major finding from analyses of the recent financial crisis from a
systemic perspective (Allen and Carletti, 2010; Cukierman, 2011; IMF, 2009; UNCTAD, 2009)
is that principal problems originated due to inherent information asymmetry among
interconnected financial agent institutions and propagated significantly via information
uncertainty in the financial markets. The financial agents had not recognized this susceptibility to
informational uncertainty—as a systemic dimension—sufficiently in advance. The lack of
transparency about the system’s level of stress and its causes, however, makes it difficult to
assess the nature of a developing stress episode adequately, design efficient crisis management
strategies, and, from a broader point of view, prevent future systemic crises. The first function of
a macroprudential policy tool is therefore to measure systemic conditions in a way that provides
a continuous signal of stress and broad coverage of the system’s areas.

Identification of systemic imbalances—the notion of financial imbalances has long
been intertwined with financial stability (Schinasi, 2004; ECB, 2005). In the context of a
financial system’s stability, accumulation of imbalances as vulnerabilities characterizes the
system’s transition to unstable state, whether through an endogenous process or due to

8 Borio (2003), p. 2.
°° Hannoun also includes goals of aggregate demand management and building fiscal buffers as well as several
goals extending the cross-sectional objectives. The first of these—limiting system-wide currency
hes—can be considered a of cro: tional common exposures objectives. The second—

strengthening infrastructure resil —can he consi da of the cross-sectional interconnections
objectives.


exogenous shocks.*” The European Central Bank (2005) defined financial stability as the ability
of “financial intermediaries, markets, and market infrastructures... [to withstand] shocks and the
unraveling of financial imbalances.” As early as 2001, Borio et al. (2001, p. 42) urged that
“{p]olicymakers need to be able to respond to the development of financial imbalances that have
adverse implications for the business cycle and financial stability. The costs of not doing so can
be very high.” Lowe (2001, p. 30) noted that policy seeking to contain financial imbalances can
reduce “the probability of bad outcomes that are associated with financial instability.”
Accordingly, the pursuit of financial stability objectives necessitates that policy regimes
explicitly address the “the build-up of financial imbalances... [lest they] may unwittingly
accommodate their expansion (Borio 2009, p. 37)

Forward-looking and forecasting capacities— there is a well-established consensus
that forward-looking capacity is integral to macroprudential policy. In the 1996 theoretical
assessment of banking system fragility, Gonzalez-Hermosillo argues that a macroprudential
measure of financial system’s stability must be forward-looking based on the probability that the
banks will remain solvent following a destabilizing shock.” In the late 1990-s and early 2000-s,
several International Monetary Fund studies emphasize conceptual development and
implementation of the forward-looking capacity in macroprudential mandates. The forward-
looking capacity is consistently included in the macroprudential strategic sets developed by the
central banks in Asia and Australia following the 1997 Asian crisis.” E.g., in Hong Kong, the
Monetary Authority adopts “a forward-looking stance in supervision, [so that the] risks inherent
in the business activities of banks can be identified early and mechanisms to manage such risks
will be set up to deal with them effectively as they arise.”"* Y am (2007, p. 34) stresses that the
forward-looking perspective constitutes “[t]he essence of macroprudential surveillance... It aims
to assess risks which might potentially occur. Risk-based supervision also aspires to have a
forward-looking perspective to risk, and aims to prioritize supervisory resources based on the
risks presented by an individual institution to the financial system as a whole.”

Summarizing the conceptual, theoretical, and empirical advances in the past twenty years,
Intemational Monetary Fund (2011) has issued a series of comprehensive studies of
macroprudential policy tools that specifically address their objectives and strategic capacities.
The studies prominently underscore the importance of the forward-looking capacity of
macroprudential tools. IMF (2011, p. 15) asserts that “[f]or macroprudential policy purposes,
aggregate risk monitoring should be robust, forward looking, and contrarian.”

Differentiation of excessive exposures and sensitivity to systemic risk posed—the
objectives of limiting probability and severity of systemic failure and strengthening
infrastructure predicate on the sensitivity to systemic risk and on the capacity to distinguish
excessive exposures (see Fig. 1). De Bandt and Hartmann (2000, p. 5) trace the emergence of
this idea in early systemic risk literature. The authors focus on the distinction between systemic
risk origination due to contagion versus common shocks and find that literature experiences

3° Schinasi (2004), pp. 8-10.

Borio and Lowe (2002a, 2002b, 2004) provide empirical evidence that financial imbalances help predict
systemic distress, while Bailliu et al. (2012) examine theoretical optimality of policy response to financial
imbalances and review theoretical precedents (e.g. Angelini et al., 2011; Boivin et al., 2010; Christensen et al.,
2011; and Kannan et al., 2009).

Gonzalez-Hermosillo (1996), pp. iii-2.

33 Kardar (2011), CGFS (2010), Lim etal. (2011a, 2011b).

34 Yue (2001), p. 237.

“general difficulty to develop empirical tests that can make a clear distinction between” them.
The early studies of systemic risk origination explore origination induced by common shocks
(e.g. King and Wadhwani, 1990; Masson 1999a, 1999b) and common macroeconomic factors”
(e.g. Calvo and Reinhart, 1996; Kodres and Pritsker, 1999).

The research on common exposures as a critical macroprudential idea has made
important advances in the early 2000s. Acharya (2001)** proposes a theoretical study of a
proposed prudential approach that separately considers exposures to systematic and idiosyncratic
risk factors. “For any given level of individual bank risk, correlation-based regulation would
encourage banks to take idiosyncratic risks by charging a higher capital requirement against
exposure to general risk factors.”*” Elsinger et al. (2002) consider correlated exposures (as a
source of systematic risk) and interbank exposures (as a source of contagion) in assessing the
financial stability of the Austrian banking system and “find that correlation in banks’ asset
portfolios dominates contagion as the main source of systemic risk.” Theoretical studies of
Rochet (2004, 2005) focus on “systematic risk, generated by a common exposure of banks to
macroeconomic shocks.”“? Recent theoretical studies specifically focus on common exposures as
a source of systemic risk (see Allen et al., 2012; Allen and Carletti, 2011). Wagner (2010),
Ibragimov et al. (2011), and Allen et al. (2012) explore the paradox between private optimality
of diversification across asset classes and its social sub-optimality. In these studies, systemic risk
arises as private diversification results in common exposures that amplify bank similarities and
contribute to the negative externality.

Needless to say, the 2007-2009 financial crisis provided substantial emphasis for the
critical role of common exposures in the systemic risk origination across institutions. This
emphasis has also sharpened the policy focus on operationalizing the macroprudential mandates
across the globe, the search for an international macroprudential policy consensus, as well on the
design of macroprudential instruments. Substantial contributions in continued development of
these questions are made by Borio (2009, 2010), Lim et al. (2011b), Nier (2011), and De Nicolo
et al. (2012). Borio (2009, p. 31) clearly lays out the operational elements of the cross-sectional
agenda, “concemed with how aggregate risk is distributed in the financial system at a given point
in time. The policy issue here is how to calibrate prudential instruments so as to address common
exposures across financial institutions and the contribution of each institution to system-wide tail
risk.” “This calibration can help ensure that each institution pays for the extemality it imposes on
the system.”“° De Nicold et al. (2012, p. 12) expand the analysis of the relationship between the
common exposures and time-varying requirements, pointing out that “Basel III also allows for
adjusting risk weights in order to control exposures to specific assets.” The authors suggest
additional sources of possible time-varying responses to common-exposure-induced
extemalities. These include a) capital requirements, b) liquidity requirements, c) restrictions on
activities, assets, or liabilities, and d) taxation.

In the United States, traditional microprudential supervisory guidance includes aspects of
critical cross-sectional macroprudential elements. Bernanke (2008) states that “the [US]

%> In this approach, the notion of common exposures is studied through distinct asset classes, for example common
investments in Lagunoff and Schreft (1998) and interbank exposures in Furfine (1999).

Acharya (2001) mimeo has been subsequently published as Acharya (2009).

37 Acharya (2009), p. 227.

si Elsinger et al. (2002), p. 2.

39 Rochet (2005), p. 108.

“0 Borio (2010), p. 3.

regulators were concerned not only about individual banks but also about the systemic risks
associated with excessive industry-wide concentrations (of commercial real estate or
nontraditional mortgages) or an industry-wide pattern of certain practices (for example, in
underwriting exotic mortgages). ... [T]heir task is to determine the risks imposed on the system
as a whole if common exposures significantly increase the correlation of returns across
institutions.” Bair (2009) and Bernanke (2009) discuss that the design of the US macroprudential
mission includes cross-sectional “monitoring large or rapidly increasing exposures—such as to
subprime mortgages—across firms and markets, rather than only at the level of individual firms
or Sectors”, as well as “analyzing mutual exposures... for possible spillovers.”

In the European Union, the importance of macroprudential focus on common exposures
is keenly recognized. In particular, EU regulators are concemed with “significant risks to
financial stability [that] can emerge when systemic risks identified at the national level may
impact other jurisdictions through spillover effects and common exposures of financial
institutions."! Accordingly, the European Systemic Risk Board (ESRB 2012, p. 2)
recommendations emphasize “closer measuring and monitoring ... [to] help authorities ...in
encouraging credit institutions to take necessary ex ante measures to correct distortions in risk
management and in limiting excessive exposures. From the macro-prudential viewpoint, it is
important that this is done at the level of the banking sector as well as at the level of individual
firms.”

3.3. Forms of Macroprudential Policy Tools

Ina series of IMF papers, Lim et al. (2011a, 2011b) survey the forms and global usage of
macroprudential tools (See Fig. 2 and Table 1). Within the current variety of forms of
macroprudential tools,” Lim et al. (2011b) find evidence that most tools are capable of reducing
pro-cyclicality, although their usefulness “is sensitive to the type of shock facing the financial
sector.” This empirical finding sustains the intuition that the effectiveness of the macroprudential
tools should be limited by their functional ability to identify conditions of systemic risk and
imbalances. The authors also find evidence of inherent limitations in some tools and analyze the
“conditions under which macroprudential policy is most likely to be effective.” Their results
support the idea that success of macroprudential tools is increased when the adaptive context of
the financial system is addressed. Specifically, they propose that macroprudential efficacy is
increased when usage includes 1) multiple tools, 2) targeted tools with higher ability to
differentiate among exposures, 3) time-varying tools that can be adjusted through the range of
financial conditions, 4) dynamic tools accompanied by clear rule-based communication, 5) tools
that coordinate well and reinforce associated policy initiatives."

Insert Fig. 2 about here

Insert Table 1 about here

“ Constancio (2012).
“2 Including early warning systems, asset price models, stress testing, and microprudential feeds.
‘3 Lim et al. (2011b), pp. 4-5.

3.4. Policy Evaluation in Adaptive Financial System

In his seminal critique of econometric policy evaluation, Lucas (1976, p. 20) argued that
“that the features which lead to success in short-term forecasting are unrelated to quantitative
policy evaluation, ... and that simulations using these models can, in principle, provide no useful
information as to the actual consequences of alternative economic policies.” In the context of the
adaptive financial system, Lucas emphasized the point that the short-term success of econometric
forecasting in capturing past change in the system had no policy value, since the system will
change in the future in response to a policy change, resulting in “deviations between the prior
‘true’ structure and the ‘true’ structure prevailing afterwards.” To remedy this limitation, Lucas
suggests the use of adaptive forecasting where policy must take into account the adaptive
behavior of the economic agents.

Similarly to the Lucas advocacy of policy adaptation, social science research on public
policy has long emphasized the view that policy is a process. In his study of the theories of the
policy process, Sabatier (1991) emphasizes a fundamental point from these theories—that public
policy requires an understanding of the adaptive behavior of the economic actors."°

Brock et al. (2003) discuss the relation between the evaluation of economic policy and
uncertainty about economic structures. The authors extend and generalize Brainard’s (1967)
classic work on policy effect of model uncertainty in macroeconomic setting. They develop
suggestions for policy evaluation where fundamental disagreements exist as to the determinants
of the problem under study, specifically incorporating model uncertainty into policy evaluation.
The premise of the research is the position that policy evaluation depends on a) policymakers
objectives, b) conditional distribution of outcomes given a policy and available information.
Therefore, policy evaluation is linked to decision-theoretic questions arising from different types
of uncertainty. The authors express uncertainty about suitability of a policy as a loss function“®
and propose a methodology of robustness analysis that considers the change in optimal policy
with respect to a change in one of the parameters in the density of model factors. The authors
account for different types of uncertainty as construction elements of the model space,”” theory
uncertainty, specification uncertainty, “* and heterogeneity uncertainty.*? In the accompanying
comments, Leeper suggests that a dynamic extension of Brock et al. (2003) would a) confront
the Lucas critique, b) allow the modeling of learning, and c) allow ongoing policy analysis in
adaptive settings, “as new policy problems arise and the economy changes.” 5

In summary, classic literature on policy evaluation suggests that in the context of the
adaptive financial system, the uncertainty in macroprudential policy can be addressed adaptively
(Lucas, 1976), incorporating the behavior of economic agents (Lucas, 1976; Sabatier, 1991), asa
continuous dynamic process (Sabatier, 1991), and considering the policymakers loss function
(Brock et al., 2003) with the corresponding and ongoing (Leeper and Sargent, 2003) robust
analysis of model uncertainty space.

“Lucas (1976), p. 41.

Sabatier (1991, p. 151) concludes that “From a policy perspective, the most useful body of work within this
tradition has been that of Elinor Ostrom and her colleagues because it combines an actor-based ive with
attention to institutional rules, intergovernmental relations, and policy decisions.”

In other words, considering the benefits and costs of the policymaker’s choice.

Brock et al. (2003), pp. 268-272.

For example, lag length for vector autoregressions.

As the extent to which different observations are assumed to obey a common model.

50 Leeper and Sargent (2003), pp. 302-307.


New research on the dynamics of feedbacks in financial systems poses additional
challenges for macroprudential policy. The key problem is the greater understanding of the
dynamic effects and the variety of the transmission mechanisms by which regulatory policies
may feed back into financial system and for which there is significant theoretical and empirical
evidence.°! This evidence shows that financial systems have a number of elements with
procyclical response to various shocks. Under shocks, these elements can initiate a dynamic
sequence from being shock absorbers into shock amplifiers. Many types of systemic feedbacks
form dynamic responses to excitations of a system influencing the system’s stability over time
(See Table 2). They are time-dependent, mostly non-linear, and multi-step processes determining
the relative states of stability or instability of the system. Systemic financial feedbacks may
originate from endogenous or exogenous incentives, and are propagated via interactions within
the system (May and Arinaminpathy 2010). The cumulative causal outcomes of feedback effects
may range from amplification (procyclicality) in cumulatively positive feedbacks to dampening
(countercyclicality) in cumulatively negative feedbacks, with generally complex, asymmetric,
time-dependent patterns in complex multi-loop feedback mechanisms.

Insert Table 2 about here

Consistent with the above theoretical perspective, modem policymaking practice
recognizes the complexity of the adaptive economic setting. Bemanke (2004) discusses two
types of central bank policies that incorporate behavioral considerations: feedback policies and
forecast-based policies. Under a feedback policy regime, “the central bank's policy
instrument...is closely linked to the behavior of a relatively small number of macroeconomic
variables, variables that either are directly observable ... or can be estimated from current
information.”*” By contrast, “under a forecast-based policy regime, policymakers must predict
how the economy is likely to respond in the medium term—say, over the next six to eight
quarters—to alternative plans for monetary policy.”*? The adaptive challenge of policymaking is
addressed through three distinct features of regulatory policies, preemptive policymaking,
structural monitoring, and risk-management approach.™ In particular, under the risk-
management approach to policymaking, “a central bank needs to consider not only the most
likely future path for the economy but also the distribution of possible outcomes about that path.”
To reach a policy decision, the regulators must evaluate “the probabilities, costs, and benefits of
the various possible outcomes under alternative choices for policy.” Under “risk-management
policy paradigm,” the regulators “may at times... undertake actions intended to provide
insurance against especially adverse outcomes.”

4, USE OF SYSTEMIC RISK EARLY WARNING TOOLS FOR POLICY

The global financial crisis has propelled a powerful wave of new research in early
warning tools for systemic risk. This research brings with it a wealth of new findings and
creative empirical ideas. Some of the notable recent papers include Davis and Karim (2008a,
2008b), Melvin and Taylor (2009), Barrell et al. (2010, 2012), Rose and Spiegel (2011), Alessi
and Detken (2011), De Nicolo and Lucchetta (2011), Babecky et al. (2013), Slingenberg and

For overview and further references see Bijlsma et al. (2010) and Gramlich and Oet (2012).
2 Bernanke (2004).

53 Bernanke (2004).

5 Greenspan (2004), Bernanke (2004).

55 Greenspan (2004).

Haan (2011), and Zhu et al. (2012). In this study, we particularly focus on five studies of
systemic early warning that resulted in macroprudential policy proposals: Frait and K omarkova
(2011), Schoenmaker and Wierts (2011), Sinha (2011), BOE (2011), and [Authors Removed]
(2013a, 2013b).

In the discussion that follows, it is useful to define the usage of certain terms. Policy
strategies are broad methodologies pursued by the regulators to achieve their objectives (e.g.
countercyclical capital, defensive buffers).”° Policy instruments are specific actions tied to
measurable economic factors. Limit strategies pursue factors that can be directly controlled by
economic agents. Target strategies pursue factors that cannot be directly controlled, but can be
indirectly influenced. Limits involve factors that are typically, but not necessarily, adverse.
Targets involve factors that are typically, but not necessarily, defensive.

Tables 3-5 compare the strategies and instruments of various proposed macroprudential
mandates. As shown in Table 3, in the time dimension, all mandates generally maintain a
consistent set of strategies for all phases (stable, ex-ante, critical, and ex-post phases) and
objectives (prevention and mitigation for both short- term and long- term).*” In the cross-
sectional dimension, consistent but distinct strategy sets characterize the pursuit of the imbalance
and interconnectedness goals. The discussion of potential uses of early warning tools, to borrow
Holland’s (1988) term, “is recklessly egotistical” in relying on the features of the Systemic
Assessment of the Financial Environment (SAFE) framework ([Authors Removed], 2011 SAFE,
2012, 2013a, 2013b) to illustrate the macroprudential applications.°® As discussed below, the
systemic EWS model basis results in a substantial comparative richness in the feasible tactical
instruments in the time- and cross-sectional dimensions.

Insert Table 3 about here

Insert Table 4 about here

Insert Table 5 about here

4.1, A Tour of a Systemic Risk Early Warning System”

The systemic risk EWS contains two components: first, a measure of systemic conditions,
and second, a set of factors that are able to explain this measure. For example, a financial stress
index (FSI) serves as a useful measure of systemic conditions by providing a continuous signal
of financial stress and broad coverage of the areas that could indicate it. Economically, financial
stress is defined to be “observable, continuous manifestations of forces exerted on economic

See BCBS (2010a, 2010b).

The seeming exception of Frait and Komarkova (2011) strategies of buffer release and capital injection are forms

of time-varying targets strategy.
BOE (2011) discusses the strategic features of a number of instruments in use across the globe, proposed for the
BOE macroprudential mandate. Mandate implementation would probably involve the capacities of BOE-
developed model basis, e.g. the RAMSI framework.

°° [Authors Removed] (2012, 2013a, 2013b).

agents.”®° Guided by the empirical evidence from Systemic risk early warning literature, the
EWS may use financial imbalance theory to explain financial stress.’ Financial imbalances are
defined as deviations of financial variables from their mean, so they represent pressures in the
financial system.

A responsive EWS methodology uses daily public market data collected from different
sectors of the financial markets and employs some dynamic weighting method to capture the
changing relative importance of the different sectors. While stress is always present in the
financial system, significant stress is identified by observations of extreme co-movements of
stress components across all markets. The FSI provides stress grades to allow interpretation of
significant stress. These stress grades are modeled and calibrated against independent
benchmarks of distress in each of the financial sectors. The EWS explains financial stress in the
markets as a build-up of aggregate imbalances of financial institutions (the agents). The
imbalances represent changing microeconomic responses of individual institutions. Thus, the
EWS supports the macroprudential functions of identification: first, to detect financial system
stress, and second, to spot institutional financial imbalances.

The EWS constructs the agent imbalances using z-scores. An imbalance x, is defined as a
deviation of some explanatory variable x, from its mean. That is, each x, explanatory variable is
aggregated, deflated (typically by a price-based index), demeaned, and divided by its cumulative
standard deviation at time t. The resulting z-score is designated x,. By construction, x, describes
imbalance as the distance in standard deviations from the mean of the x, explanatory variable.

The system allows forecasting of developing vulnerabilities. To mitigate inherent
uncertainty, the EWS develops a set of medium-term forecasting specifications that gives
policymakers enough time to take ex-ante policy action and a set of short-term forecasting
specifications for verification and adjustment of supervisory actions.

The institutional imbalances may consist of several classes of institutional exposures: e.g.
risk, retum, liquidity, and system structure. In each exposure class, the EWS includes imbalances
with significant statistical and Granger properties in explaining financial stress in the past. The
EWS explains financial stress using both public and proprietary supervisory data from
systemically important institutions, regressing institutional imbalances using an optimal lag
method. The institutional imbalances are selected considering their optimal lag characteristics,
based on the notion that shocks to various agent exposures take varying amount of time to
precipitate to the conditions that materially change the agents’ market behavior—i.e. the
conditions tied to the financial markets’ stress. The EWS considers two sets of these imbalances:
those that historically have taken relatively short time (from one to six quarters) to precipitate
into the financial system stress, and those that take relatively longer. The two modeling
perspectives have distinctly different functions and lead to different model forms. Short-lag
models function dynamically, seeking to explain stress in terms of recent observations of it and

60 [Authors Removed] (2011 CFSI), p. 12. Illing and Liu (2003, 2006, p. 243) examine financial stress “as a

continuous variable with a spectrum of values, where extreme values are called a crisis.” This concept of
financial stress extends Bordo et al. (2000) notion of “an index of financial conditions” which studies whether
aggregate price shocks are useful for dating financial instability.

It was developed by Borio et al. (2002a, 2002b, 2004, 2009) for aggregate macroeconomic imbalances. Borio
and colleagues study the relationship between “banking distress” and
such as imbalances in credit-to-GDP, property prices, and equity prices. [Authors Removed] (2011 SAFE,
2013a, 2013b) apply imbalance theory to institutional data.


of institutional imbalances that tend to produce stress relatively quickly and with a short lead.
Long-lag models seek to explain the buildup of financial stress well in advance, in terms of
institutional imbalances that tend to anticipate stress with a long lead. For each of the two
forecast horizons (short-lag and long-lag), the respective EWS forecast combination highlights
the most persistent features of the institutional imbalance models in explaining and forecasting
financial system stress.

In the EWS, some imbalances are adverse: that is, the larger the deviation of such an
imbalance, the greater is the potential shock. Therefore, systemic financial stress tends to
increase with the rise in adverse imbalances. Other imbalances are defensive: systemic stress
tends to decrease with the rise in defensive imbalances. A cross institutions, the EWS
distinguishes excessive exposures for adverse imbalances and sufficient exposures for defensive
imbalances based on its historical association of imbalances with stress. In addition, the EWS
establishes and updates thresholds for each imbalance that are associated with stress migration
across distinct grades of systemic stress. The EWS is also a learning environment, in a sense that
these excessive and sufficient exposures change over time as the financial system adapts over
time. In each temporal regime (long run and short run), the EWS highlights imbalances that have
strong positive and negative associations with financial stress. This enables a focus on
imbalances that are sensitive to the financial agents’ contributions to systemic risk across time.

4.2. Macroprudential EWS Use for Time-Dimension Objectives

4.2.1, Time phases and policy

Tactical applications of a systemic risk EWS in the time dimension reflect the
fundamental aspects of supervisory financial stability policies as a function of the time varying
level of financial stress. Policymaker’s possible actions are, therefore, predicated by two
conditions: first, their existence in the space of available macroprudential strategies”, and
second, their ability to identify stress concurrently. Thus, the key feature of a systemic EWS
driving any tactical policy applications is its capacity to differentiate stress aggregation across
time. In particular, the EWS should be able to differentiate the various phases of a stress cycle.

From a purely conceptual standpoint, it is reasonable to distinguish four time phases of
stress cycle: ex-ante stability, ex-ante escalation, systemic stress, and ex-post (see Fig. 3). Each
phase is characterized by a specific pattern within certain ranges of stress values.

Insert Fig. 3 about here

The ex-ante stability phase is characterized by fluctuations of stress within a historically
normal range of stress values. Occasionally during this phase, stress may also decline into the
below-normal range. This may be due to a number of factors like financial system growth,
technology changes, financial agent optimism and expectations, etc. Generally, the episodes of
below-normal stress tend to be brief, as financial agents find it profitable to increase their risk
appetites quickly with a corresponding elevation of stress into the normal range. In this
hypothetical stress cycle, the ex-ante stability phase should immediately follow the ex-post phase
and be followed by the ex-ante stress escalation phase. The latter is characterized by dual
increases in the level and rate of financial stress. During this phase, stress migrates from normal
to moderate range of stress values.

52 The strategies in turn have to exist in the space of financial stability objectives.

The critical—systemic stress—phase is characterized by movement of stress within
moderate to significant range of historical stress values. The ex-post phase generally follows the
systemic stress phase and exhibits stress anywhere from normal to below normal range. This
phase may or may not be distinctly different from the stability phase. To the extent that
differences exist, the ex-post phase is characterized by the “rough landing” pattern of stress
following a period of systemic stress, when small “after-shocks” may have inordinately
amplifying effect on stress until the financial system settles into the new stability phase.

Thus, a macroprudential tool forming a basis for tactical actions in the time dimension
should be able to identify a phase of relative stability, an ex-ante phase of stress increase, a
critical phase, as well as an ex-post phase of reestablished stability. Considering this, it is clear
that the policymakers’ choice of policy actions in the time dimension is assisted by establishing
the decision rules defining the ranges and the thresholds corresponding to the ranges of systemic
stress. The decision rules then allow differentiation of stress phases among the volatile time
patterns of stress, while stress thresholds facilitate the identification of systemic stress episodes
among the phases. To proceed, it becomes operationally convenient to consider the zones
between thresholds as grades of stress. Given an imbalance-based systemic risk EWS, it can be
shown that policymakers’ decision process is assisted by finding stress target policies and
imbalance action thresholds. Accordingly, a similar approach of retrospective EWS forecasts in a
series of historical stress episodes can establish the stress targets and action thresholds useful for
supervisory policy. When the forecast of concurrent stress is below a target action level, this
approach supports a policymakers’ laissez-faire decision to let the markets’ self-resolve. When
forecast of stress exceeds the target level of stress, this approach enables a policymakers’ risk
management process to weigh the economic costs of regulatory action against economic costs of
a shock.

The architecture of a systemic risk EWS provides policymakers two time horizons (short-
term and medium-term) and two channels for macroprudential actions in the time dimension.
The first channel includes actions related to the measure of financial stress. The second channel
includes actions related to the EWS model of institutional imbalances. While the index measures
current stress in the markets, the model forecasts future stress. Both channels are the product of
the common design objective of systemic risk EWSs: that of contributing to financial system
stability through the development of tools that inform monetary policy and supervisory policy
actions. Accordingly, a financial stress measure seeking to aid macroprudential policy should
effectively differentiate among areas and factors of stress origination. This financial stress
measure (FSI) includes the time pattern of systemic stress and finds, based on probit regression,
that the pattern optimally corresponds to four stress grades matching the conceptual time phases
of a systemic stress cycle (see Fig. 4).

63 See [Authors Removed] (2012), who investigate the choice of stress grade targets and action thresholds based
on the US empirical evidence.

°! The Cleveland Financial Stress Index (CFSI) in the SAFE EWS

CFSI currently differentiates among six financial market sectors (credit, foreign exchange, equity, interbank,

real estate, and securitization) and sixteen origination factors. See [Authors Removed] (2011 CFSI, 2012) for

discussion of the Cleveland Financial Stress Index and comparative discussion of other US financial stress

measures. These measures show alternative allocations of sectors and factors of the US financial system.

By comparison, Bordo et al. (2000) suggest a five-category differentiation of distress, with a refinement of the

below normal stress grade into two categories: “moderate expansion,” and “euphoria.”

Insert Fig. 4 about here

As shown on the right-hand-side vertical axis of Fig. 4, the FSI provides the probabilities
of a systemic stress episode, given the particular level of stress. For example, the May 2012 FSI
level is very close to zero, falls into the normal stress grade, and implies that the probability of
this stress being a part (e.g. being the “‘on-ramp”) of a systemic stress episode is no greater than
8.7%. The vertical bars in the chart represent incidents of well-known stress episodes.”’ The four
identified stress grades serve to establish thresholds that differentiate among the time phases of
stress (see Table 6).

Insert Table 6 about here

Using the observable market phenomena, FSI informs the policymakers and the public
about the aggregate level of financial stress in the system. In addition, the index is useful for
structural monitoring.” Lastly, the risk management aspect of central bank policies is helped by
the EWS strategic capacity of forecasting of stress across time. Specifically, the EWS estimates
stress in the financial system at a future point in time with a medium-term (12 to 18 months)
forecasting horizon, enabling the regulators to weigh the risk-management implications of
proposed policies. In addition, the EWS provides an additional set of near-term forecasts for
verification of policy actions.°? The typical graphic output from the EWS out-of-sample forecast
is shown in Fig. 6. The graph compares the actual realized stress index (solid line) against the
two sets of EWS forecasts: near-term using short lag imbalances (dashed lines) and medium-
term using long lag imbalances (dotted line).

Insert Fig. 5 about here

Insert Fig. 6 about here

Consideration of the macroprudential strategies in the time dimension in various
macroprudential mandates reveals a great degree of consensus (see Table 3). The set of strategies
includes in the descending order of frequency: time-varying limits (5), time-varying targets (5),
time-varying risk weights (3), disclosure (2), communication (1), identification of stress phase
(1), guidelines for monitoring (1), distribution restrictions (1), time-varying charge (1), and asset
rate rules (1). These strategies are stated broadly and allow policymakers further refinement. For
example, guidelines for monitoring can specifically include stress level and rate of change
monitoring ({[Authors Removed], 2013a, 2013b), or time-varying targets can include
countercyclical buffers, time-varying provisioning, and time-varying reserve requirements (Frait
and Komarkova, 2011).

67 [Authors Removed] (2011 CFSI, 2012) find the index to be responsive to stress episodes and a reasonably good
identifier of systemic financial stress.

5 See Section 3.4. [Authors Removed] (2012, p. 29) analyze US FSI series for structural breaks using Quandt
likelihood method (Quandt, 1960). They find of evidence of two breaks (see Fig. 5): “The first break, indicated
likely in August 1998, corresponds to the announcement of Financial Services Act passage by the U.S. Senate,
leading up to the U.S. Financial Services Modernization Act later in the year. The second break, indicated likely
in July 2007, corresponds to mounting frictions in the financial markets that would result in the financial crisis.”

59 See [Authors Removed] (2013a, 2013b) for the discussion of out-of-sample accuracy of EWS forecasts.

In general, macroprudential policies in the time dimension seek a set of remedial actions
to “create built in mechanisms that attenuate the impact of procyclical behavior.””° Further
tactical details emerge in consideration of the strategic objectives that arise during the distinct
time phases of stress cycle. In the stability, ex-ante, and ex-post phases, the principal
responsibility of regulators will be the prevention of systemic stress increase. To the extent that
the regulators can influence the propagation of stress from an ex-ante phase to the critical phase,
their macroprudential actions would be designed to inhibit stress increase. In the critical phase,
the principal responsibility of policymakers is mitigation. Thus, in the time phases following the
prevention strategy, the macroprudential actions focus particularly on instruments that enable
careful monitoring and disclosing levels of stress and imbalances. In the critical phase, following
the mitigation strategy, the macroprudential actions focus on instruments that enable reduction of
adverse stress impacts.

4.2.2. EWS instruments in the time dimension”!

Short-term macroprudential EWS tactics ought to be exercised during the ex-ante
escalation, critical, and ex-post phases. The corresponding EW S-based instruments support the
relevant strategies of disclosure and stress and imbalance identification. Based on the current
EWS capacities, the use of seven instruments in the ex-ante escalation stress phase is suggested:
1) disclosure of stress grade, 2) disclosure and identification of stress phase, 3) stress level and
rate of change monitoring, 4) disclosure of public forecasts, 5) disclosure of public imbalances,
6) guidelines for monitoring short run imbalances, and 7) communication of private imbalances
and macroprudential limits.

Critical phase tactics emphasize disclosure of instruments that ameliorate existing stress
and communication of adverse imbalance limits and defensive buffers. The population of
adverse imbalances is dynamic and time-dependent. In addition to the seven ex-ante escalation
instruments, a number of defensive imbalances may be targeted for policy instruments besides
the capital and liquidity buffers. Four additional instruments are suggested to help reduce stress:
1) appropriate interest rate defensive exposures and plain vanilla hedges, 2) credit risk distance
targets, 3) solvency targets, and 4) institutional liquidity targets. The supervisors would utilize
macroprudential EWS analysis to establish pre-targeted minimums that institutions must
maintain in systemic stress phase.”” Ex-post phase continues to emphasize the disclosure
instruments 1 through 5.

Long-term macroprudential EWS tactics can be exercised during the stability and ex-ante
escalation phases and utilize instruments based on the long run institutional imbalances. The
seven short-term policy instruments suggested above are also used for the long run objective.
However, given the long run objective, these instruments would use different long run limits and
targets to encourage countercyclical risk management. Specifically, long run EWS instruments
could include four new instruments of recommended targets for 1) positive-relative-to-inflation
interbank currency exposures, 2) balance sheet liquidity under adverse stress scenarios, 3)
institutional interest rate defensive exposures, and 4) credit risk distance measures.

Cukierman (2011), p. 30.
7\ See Table 7 and Table 8.
Perhaps subject to prompt corrective action.

Through its specific design for the prudential objectives of monitoring, alerting, and
forecasting”’, the FSI acquires strategic capacities important for policymaking. These capacities
reflect its functions to: 1) improve supervisory monitoring of emerging stress in the financial
system, 2) produce alerts when stress reaches certain thresholds,” 3) aid short-term systemic
stress projections. A dditional useful capacities also stem from the intrinsic design features of the
FSI: its continuity and transparency to the underlying market components.

Potential EWS instruments of identification—EW S identification instruments include
stress components, stress grade, stress grade thresholds, stress level, one period rate of change,
and two period rate of change.

The initial step in determining the potential response of supervisors to financial market
stress ought to be an analysis of the degree of stress. The FSI offers continuous and
contemporaneous identification of financial stress. Further, the construction of the FSI stress
grades helps supervisors to address the question of stress severity. It is important that policy
response to financial stress not be mechanical but well considered. Among others, these
considerations can include the following three FSI features: current stress grade, FSI signaling,
FSI rate of change trends, and FSI projections.

First, the policy response should anchor to the current specific grade. For instance, only
twice has the financial system been in Grade 4. These periods were unique and the policy
responses (both monetary and supervisory) necessitated extraordinary measures. Historically,
policy responses during the times of moderate stress (Grade 3) have been both more common
and less aggressive then crisis policies. Thus, it would be more appropriate to expect that the
bulk of regulatory policies would be undertaken tied to specific observations of moderate stress.

Second, it is also beneficial for the supervisor to monitor the origin of stress by analyzing
the components of the FSI. This is rather difficult, because systemic financial stress generally
exists concurrently in multiple markets. Stress in individual market has the potential to become
systemic by influencing other markets, i.e. propagating. Any consideration of specific policy
actions should carefully consider the particular markets affected and stress origins within these
markets. Here, a signaling rule provides a valuable roadmap: for example, “systemic stress is two
consecutive periods of distress above previous period thresholds, or concurrent distress in at least
two distinct markets. This operational guide enables the supervisor to observe significant stress
alerts both within a particular market and in the system, as stress signals that begin propagation
through several markets. This identification offers ... significant time advantage in the
interpretation of observations of the financial system stress.””° Fig. 7 shows a sample application
of such a signaling rile to FSI. In addition, current trends in the FSI, which include the rate of
change of FSI with respect to time, FSI “| dFSI/dt, provides useful information.”° If the
supervisor identifies the current episode, or its features (e.g. FSI and FSI) as systemic, the finding
could be sufficient to warrant further careful consideration of policy responses. To illustrate,
consider the FSI series from 4Q 1991 to 4Q 2011. Analysis of FSI trends would alert
policymakers to the significant developing stress in the 3Q 1998 (the advent of LTCM crisis) and

3 See [Authors Removed] (2012).

For example, chosen thresholds can include grade boundaries with established dardized distance

or be based on targeted implied systemic stress probabilities.

[Authors Removed] (2012)

[Authors Removed] (2012) consider FSI over one period, FSI over two periods, and FSI acceleration (steepness
of the rate of change—FSI “! dFSI/dt).


the 3Q 2007 (the advent of Subprime crisis). The two-period intertemporal rate of change of FSI
(FSI) at these points showed a movement of 1 standard deviation or more: 1.0 std and 1.3 std
respectively (see Fig. 8 and Fig. 9). However, the finding of systemic stress”’ on its own may not
constitute a necessary condition for policy action.

Insert Fig. 7 about here

Insert Fig. 8 and Fig. 9 about here

Although FSI is designed for prudential applications, it also gives the public a set of
dynamic strategies. Therefore, supervisory uses of FSI should consider its different potential
users and, in tum, its dynamic effects, including the potential positive and negative feedbacks
induced by the FSI information. Its direct users may include supervisory and monetary
policymakers. Indirectly, FSI”* informs the financial market authorities, the financial institutions,
as well as government fiscal policymakers. The dynamic effects in this setting would stem from
the relationship of the financial institutions’ strategies to the expectations of direct and indirect
policy actions. The policymakers can further explore the resulting information feedback to
amplify or balance desired policy actions.

Potential EWS guidelines for monitoring—Potential EWS monitoring guidelines can
serve to establish consistent standards of interpretation of the EWS results and to form a basis for
further disclosure and communication instruments. In addition to clarifying the stress level and
rate of change, these guidelines could include short run imbalances and transition matrices.

It can be shown that FSI exhibits important autoregressive properties and that different
Granger property patterns of interaction exist between institutional imbalances and financial
stress. ’” These Granger causality association patterns are shown in Fig. 10 with the positive
imbalance to stress relationships above the horizontal axis and the negative relationships below
the axis. The patterns of association of institutional imbalances with financial markets’ stress
allows the EWS to establish and utilize several basic monitoring models, including an FSI-based
benchmark model, and some basic short and long run models based on publicly available data
(see Table 7).

Insert Fig. 10 about here

Insert Table 7 about here

Monitoring thresholds for each imbalance associated with stress in the short run enables
policymakers to consider risk management of systemic stress. In addition to their use in internal
monitoring, the policymakers can make the monitoring guidelines available to the public through
disclosure. This disclosure would in tur allow the financial system agents to improve the short-
term risk management of their exposures to avoid system-wide stress.

The finding of systemic stress may be triggered by the rate of change of stress vis-a-vis its historical pattern or
some specific grade threshold of stress.

Including overall FSI observations, its components, and its signals of systemic or individual markets’ stress.

7 [Authors Removed] (2012, 2013a, 2013b).

The EWS basis in the interaction of institutional imbalances and financial stress also
provides the corresponding transition matrices as an additional instrument for monitoring. A
typical monitoring transition matrix describes the change of a particular aggregate imbalance that
is associated with transition of stress from one grade to another, all else held equal. A sample
transition matrix for leverage is shown in Table 8. The monitoring transition matrices may be
integrated into 1) the assessment of overall level of stress, where transition of stress components
may be observed; 2) the analysis of the contributions of individual stress components and
institutional imbalances to overall stress; and 3) the design of policy actions (whether any action
is warranted, in what area of exposure, and how to act).

Insert Table 8 about here

Potential EWS time-varying limits and targets—the EWS provides a number of
models that explain financial stress as a function of certain aggregate institutional imbalances.
Because of the dynamics of the interaction of these imbalances with financial stress, the
sensitivity of the contribution of individual imbalances does not remain static, but varies in time
as the series changes. Therefore, the instruments tuned to specific aggregated imbalance need to
be considered flexibly and beyond a pre-imposed static countercyclical schema. Specifically, the
instruments should recognize the varying weight of the imbalance’s contribution to overall
predicted financial stress. Fig. 11 shows a 1Q 2012 example of that the actions of the financial
agents result in varying sensitivities of the long-lag imbalances to financial stress. Ass this
example illustrates, among the imbalances with consistent Granger properties to financial stress
that enter the EWS models (see Fig. 10), recent evidence emphasizes those imbalances with
particularly high stress interaction sensitivities.

Insert Fig. 11 about here

Similarly, the defensive imbalances can also become the source of time-varying policy
targets, where policymakers can target financial institutions to maintain certain defensive
aggregate exposures at a certain level. For example, as Fig. 11 suggests, recent EWS analysis
supports the beneficial effect of the post-crisis institutional develeraging on financial stress.
Based on this evidence, current forecasts retain this defensive impact of deleveraging, assuming
that the interaction sensitivity with financial stress is maintained at its present level. EWS-
suggested time-varying targets include solvency level, solvency distance, capital buffer,
leverage, interbank currency, interest rate hedging, interest rate distance, institutional liquidity,
liquidity buffer, and credit risk distance.

Potential EWS instruments of disclosure—The tactical importance of prudential
disclosure instruments lies in their ability to reduce market uncertainty. However, given the
considerable variation in disclosure practices (FSB-IMF-BIS, 2011), the considerable burden of
excessive disclosure, and its information overload (Kohn, 2011)*", it may be particularly

Based on recent analysis, the suggested EWS time-varying limits would include liquidity index, aggregate
expected default frequency, interbank concentrations, and leverage. The time-varying limit instruments are also
relevant in the cross-sectional dimension, as policymakers further attribute imbalances to specific institutions
and form detailed microprudential limits.

Considering the retrospective impact of disclosure on the 2007-2009 Subprime Crisis in the United States, Kohn
(2011, p. 8) argues that “For some instruments, even if disclosure had kept up it would have been futile
instruments were so complex that the required information to appropriately monitor risks was overwhelmingly

-20-

important to clarify which exposures should be disclosed in pursuit of macroprudential
objectives.

In an important early study of “Prudential supervision to manage systemic vulnerability,”
Guttentag and Herring (1988) consider policy options to counteract financial agents’ behavioral
factors such as cognitive bias and Knightian uncertainty. They strongly advocate the use of
systemic application of supervisory stress testing. Herring (1999, p. 77) emphasizes that “the
systematic application of stress tests is perhaps the most effective defence against disaster
myopia. ... By specifying the kinds of shocks and magnitudes of shocks that banks should be
prepared to sustain, the regulatory authorities can ensure that low-probability, high-severity
hazards are not simply ignored.” Among alternative policy options, Herring (1999, p. 77) also
suggests that information releases may be useful in reducing procyclicality: “Ex-ante public
disclosure of exposures to credit risk may exercise some constraining influence.” And even if
disclosure fails to constrain the build-up of concentrations of credit risk ex ante, at least it is
likely to reduce collateral damage when disasters occur, by reducing the destructive uncertainty
about which institutions have sustained damage from the shock.” Bernanke (2004) emphasizes
the significant policy role of regulatory disclosure and communications: “Central bank
communication and transparency are important precisely because of the role of private-sector
expectations in determining the effectiveness of monetary policy.”

Disclosure instruments feature prominently among the instruments of the recent
macroprudential mandates (see Table 4 and Table 5). However, the lack of model basis, as well
as the regulatory caution over pursuing empirically unproven instruments limits the prudential
toolbox to those instruments that have already been effectively tried. Bank of England (2011)
cites the empirical evidence of the US and EU stress test disclosures to support the use of its
macroprudential instrument recommendations for targeted disclosure requirements “to reduce
likelihood of information contagion” and “to enhance resilience by limiting uncertainty about
specific exposures or interconnections.” At the same time, the authors warn that the use of
liquidity disclosure instruments should be weighed against the risks of “spooking the market or
making buffers less usable.”®° Correspondingly, the Bank of England proposed disclosure
instruments reflect this cautious approach: in addition to stress test disclosures, Bank of England
suggests ongoing enhanced disclosure of sovereign and banking sector exposures, combined with
“occasion[al]...disclosure of exposures to specific risks.”®°

The Bank of England’s careful approach to disclosure certainly parallels the caution
urged by the present study (see Section 3.4) against unguarded introduction of policy
instruments. Nevertheless, the systemic EWS model basis enables additional detail and clarifies
the design intent in the consideration of potential time-varying disclosure instruments.
Specifically, the EWS continuity permits consideration of variable instruments for the four
different temporal regimes of policy: a time of stability, ex-ante to episode formation, within a
stress episode, and ex-post.

large. Indeed excessive complexity and information overload may be limiting factors on the effectiveness of

disclosures.”

Emphasis added.

83 For additional discussion of information value of disclosure see Morgan et al. (2010), Tarullo (2011), and Kohn
(2011).

51 BOE (2011), p. 5.

85 BOE (2011), p. 19.

8° BOE (2011), p. 2!

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Systemic risk EWS-based disclosure instruments can include various information
components of the analytical framework: 1) stress identification measures (stress grade, stress
phase, stress level, stress rate of change), 2) prudential guidelines (guidelines for short-term
limits, guidelines for short run imbalances, transition matrices, and guidelines for long-term
targets), 3) risk warnings (short-term and long-term public imbalances, short-term and long-term
public forecasts), and 4) stress testing.

Policymakers may elect not to disclose certain information components when their risk
management assessments show that the benefits of transparency are likely to be outweighed by
the risks of adverse behavioral feedbacks in the markets (“spooking the markets”). However,
increased understanding of the potential disclosure instrument choices clearly benefits the
policymakers’ capacity in pursuit of financial stability in the long run.

Disclosure of prudential guidelines and risk warnings is intended to “increase
accountability” and “transparency of internal decision-making processes” “creat[ing]
commitment on the part of the macroprudential authority or its constituent agencies to take
follow-up action.”®” In addition, disclosure of risk warnings serves to enhance the capacities of
the financial agents to manage systemic risk and imbalances across the time dimension without
creating “the impression that the authority is attempting to predict crises.”® Importantly, the
disclosure of the EWS public imbalances and forecasts “can in and of itself lead to changes in
behavior of markets and institutions, potentially reducing the need for more intrusive
intervention.”® Finally, disclosure of EWS-based stress testing components” serves to reduce
uncertainty dynamically by the linking of systemic risk EWS identification measures with the
ongoing stress-testing activities and information releases to the public.

Potential EWS instr its of c ication—Macroprudential communication
instruments can contain the full set of EWS-based public disclosure instruments, including 1)
stress identification measures, 2) prudential guidelines , 3) risk warnings, and 4)stress testing. In
addition to these public EWS instruments, the regulators can also select private instruments, such
as those based on institution-specific exposures, or exposures that concern only a certain group
of institutions. By choosing to communicate regarding these instruments privately, the
policymakers both address specific microprudential concerns and avoid the transmission of
baseless anxiety to other financial agents that are not similarly exposed. Thus, by definition,
communication instruments should include those that tend to time-vary dynamically across
different groups of institutions. This approach is consistent with other current macroprudential
mandates. For example, Frait and Komarkova (2011) mandate incorporates“[a]ctive
communication with the financial markets and the public, including disclosure of stress tests
results, in order to reduce the level of uncertainty about the stability of the financial sector.”*!
Based on the above premises, the EW S-based communication instruments can also include 1)
private imbalances, 2) macroprudential limits, 3) macroprudential targets, and 4) stress testing.
Policymakers’ actions based on these instruments would include communication of findings
based on private supervisory observations of imbalances directly with the institutions and

87 IMF (2011), p. 41.

Ibid.

© Ibid.

In the SAFE EWS, stress-testing measures include the scenario-based sets of solvency distances, liquidity index
distances, credit risk distances, and interest-rate risk distances (see [Authors Removed] 2013a, 2013b).

51 Frait and Komérkova (2011), p. 105.

-22-

provision of specific guidance when the institutions approach the individual institutional limits
and targets.

The preceding discussion explained the conceptual alignment of the new systemic risk
early warning tools with the functional requirements of regulatory policies. The design of these
early warning tools is directed to facilitate the prudential objectives of monitoring, alerting, and
forecasting systemic stress. These functions correspond precisely to the preemptive, structural
monitoring, and risk-management requirements of the central bank policies.

4.3, Macroprudential EWS Use for Cross-Sectional O bjectives
Tactical applications of a systemic risk EWS in the cross-sectional dimension reflect the
macroprudential policy objectives of limiting failure across institutions and strengthening
infrastructure resilience. The key features of a systemic EWS driving its tactical applications
cross-sectionally are the EWS capacities to distinguish imbalances across institutions and to
respond sensitively to systemic risk posed through the intricate interconnections of the financial
system.

4.3.1, Cross-sectional directions and policy

A macroprudential tool with a capacity to limit severity of failure should in particular be
able to subdue the imbalances that amplify financial stress and result in excessive stress
propagation. In other words, the tool should be able to differentiate imbalances that are common
across institutions and associated with increases in systemic risk and to limit them. Doing so
would control those aggregate institutional imbalances that are able to amplify the propagation of
systemic stress. Tactically, reducing such common exposure imbalances can provide some
protection to limit the severity of failure.

Furthermore, to lower the probability of systemic failure, the macroprudential tool should
also be able to differentiate those uncommon imbalances that have some potential to propagate
financial stress across institutions. Put differently, the tactics should include identification and
monitoring of adverse cross-sectional imbalances. A financial system may consist of a set of
institutions that are diversified cross-sectionally.”* Despite the relative absence of common
aggregate imbalance, the presence of several institutions with individual exposures that interact
with financial stress with significant correlation (adverse exposures) enables a potential fora
shock-triggered ripple through the correlated group of institutions. In this system, the adverse
exposures subjected to a shock can increase the probability of systemic failure. Conceptually, the
macroprudential tactics would include identification and monitoring of these adverse exposures,
as well imposition of exposure limits to decrease the vulnerability of the affected group of
institutions to a damaging run, reducing the probability of systemic failure.

A macroprudential tool with a capacity to strengthen infrastructure resilience should be
able to distinguish sensitively the effects of direct and indirect interconnectedness of financial
institutions. As discussed above, to the extent that common or even simply correlated exposures
exacerbate the potential for systemic failure, uncorrelated exposures across institutions and time
help mitigate this potential and increase the overall system resilience. In addition, infrastructure
resilience to potential shocks is enhanced to the extent that the financial system accumulates
sufficient defensive exposures (e.g., capital, liquidity, etc.). Therefore, the tactics to strengthen

2 That is to say, at each point in time, this set of institutions maintains tends to maintain intemally offsetting

exposures. A perfectly diversified set of institutions would maintain a zero net imbalance.

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infrastructure resilience can be twofold: first, promoting uncorrelated exposure and behavior and
second, promoting defensive exposures. It follows that the resilience tool should possess both
macroprudential and microprudential capacities: macroprudential—because it considers systemic
risk aggregations across institutions, and microprudential—because it considers exposures within
an institution at each point-in-time. First, the resilience tool should be able to identify
uncorrelated behavior across institutions and provide regulators with tactical instruments to
encourage uncorrelated behavior. Second, the resilience should distinguish those exposures that
consistently reduce financial stress for both prudential perspectives, that is for the institution and
the system. A classic example of such defensive exposure is capital. To an individual institution,
accumulation of capital is a defensive mechanism to reduce vulnerability to failure. To the
financial system, accumulation of capital acts as protective buffer against systemic stress.
However, capital is not the only defensive exposure. The resilience tool should be able to
identify several of these and provide regulators with tactical instruments to encourage defensive
exposures, strengthening infrastructure resilience.

As discussed for the time-dimension tactics, the EWS analysis of Granger properties of
short-lag and long-lag institutional imbalances (Fig. 10) provides a foundation for further
modeling of time-varying adverse and defensive imbalances (Fig. 11). These imbalances can also
anchor the tactics in the cross-sectional dimension as aggregate time-varying limits (for adverse
imbalances) or targets (for defensive imbalances). In addition, the EWS allows consideration of
the microprudential aspects of cross-sectional objectives through analysis of individual firm
contributions to financial stress. As Fig. 12 shows, the cross-sectional risk topography of the
EWS is informative and allows the study of change in aggregate risk across various markets and
across time. Furthermore, the EWS provides the analytical perspective that allows common
exposure analysis for tactics to limit severity of systemic failure (see Fig. 13). In fact, Fig. 13
provides clear visual evidence of the structural break of 1998 observed earlier through the
structural monitoring capacity of the FSI (Fig. 5).

Insert Fig. 12 about here

Insert Fig. 13 about here

In a further enhancement of cross-sectional analytics, the EWS allows decomposition of
financial stress by risk element within a specific financial firm (see Fig. 14). This allows
supervisors to distinguish those institutional exposures that are idiosyncratic from those that are
systematic. Macroprudential policies of limits (for adverse exposures) or targets (for defensive
exposures) can address the exposures that are common across institutions. The idiosyncratic
exposures are unique to specific institutions and can be addressed by microprudential means.

Insert Fig. 14 about here

4.3.2, EWS instruments in the cross-sectional dimension
EWS tactics in the cross sectional dimension share common strategies with the EWS
tactics used across the time dimension. For example, in both preventive and mitigating strategic

88 See Table 7 and Table 8.

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sets, the tactics include instruments of public disclosure and private communication:™ disclosure
of 1) stress identification measures, 2) prudential guidelines, 3) risk wamings, and 4) stress-
testing; communication of 1) private imbalances, 2) macroprudential limits, 3) macroprudential
targets, and 4) stress-testing.

In addition to the common disclosure and communication instruments, EWS tactics to
limit severity of failure concentrate on two types of policy instruments: 1) identification of
common adverse imbalances across institutions, 2) time-varying limits on these systematic cross-
sectional imbalances. The identification instruments are applied by institution and include
monitoring of aggregate adverse imbalances. The time-varying limit instruments are applied to
aggregate adverse imbalances. With the transition from preventive to mitigating sets, the EWS
tactics refocus from long run imbalances and limits to short run imbalances and limits.

Similarly, EWS tactics to limit probability of failure include common disclosure and
communication instruments and focus on instruments of identification, monitoring and limiting
the aggregate adverse long run and short run imbalances. A gain, the perspective of these
instruments changes from the long run to short run perspective, as the strategies change in time
from preventive to mitigating.

EWS tactics to strengthen resilience in the event of shock to a particular exposure center
on the principal defensive means to withstand the shock. These tactics implement the two types
of strategic emphasis discussed above. The first type consists of tactics that encourage
idiosyncratic imbalances across significant institutions. In the prevention set, these instruments
include building up defensive exposures through time-varying risk weights. In the mitigation set,
these instruments include a progressive reduction in required minimum buffers and targets when
institutions show certain idiosyncratic imbalances. The second type consists of tactics that
encourage defensive imbalances. The instruments include customized versions of short run and
long run defensive targets. In addition, in the prevention set, the means of building up defensive
imbalances include time-varying risk weights. In the mitigation set, these instruments include a
progressive reduction in buffers and limits when institutions exceed defensive targets.

Among these instruments, probably the most challenging set of instrument design issues
is raised by the time-varying risk weights. This instrument is not unique to the EWS tactics. In
fact, itis common across several macroprudential mandates. Acharya (2011) explains this
instrument as implemented by the Reserve Bank of India.

“This approach requires horizontal aggregation of financial institutions—balance-
sheets and risk exposures to identify over time — say each year — which asset classes
are being “crowded in” as far as systemic risk concentrations are concerned. For
instance, if mortgages or mortgage-backed securities are increasingly picking up the
lion share of all risks on bank balance-sheets, then the regulators could proactively
react to limiting any further build-up. This could be achieved for instance by
increasing the risk weights on future exposures to this asset class....One advantage of
dynamic sector risk-weight adjustment approach is that if it is consistently
implemented by regulators and anticipated by the financial sector, then it can act as a
valuable countercyclical incentive. Financial firms anticipating the future risk in risk
weights may stop adding exposure to an asset class once it is sufficiently crowded in.
One disadvantage is that it may create a race to “get in first” and also relies heavily

°4 These instruments are common in the preventive and mitigating sets across the time dimension as well. See

Section 4.1.1.

-25-

on regulatory discretion turning out to be prescient in identifying risk pockets and
having sufficient will in good times to lean against the wind of fast-growing asset
classes.”

The EWS provides supervisors a number of imbalance-based defensive policy
instruments, in addition to the traditional capital and liquidity instruments. These defensive
imbalances tend to reduce stress propagation. Thus, the EWS enables a host of additional
policies that encourage institutions to strengthen cross-sectional resilience. For example, in the
short run the EWS highlights the benefits of interest rate defensive exposures and plain vanilla
hedges, building-up institutional credit risk distance measures, and maintaining strong
institutional liquidity. In the long run, the EWS highlights positive-relative-to-inflation interbank
currency exposures, strengthening balance sheet liquidity under adverse stress scenarios, as well
as building up institutional interest rate defensive exposures and credit risk distance measures.

5. CONCLUSION

How can a systemic risk early warming system (EWS) facilitate the financial stability
work of policymakers? The present paper explored this complex topic in the spirit of starting an
open discourse. The discussion addresses the conceptual bases for specific types of policy
applications first as functions following objectives of financial stability, then as tactical
supervisory actions. As such, policy actions become enabled by specific macroprudential tools
that satisfy the strategic and tactical requirements. These requirements target systemic stress
aggregation in two dimensions: across time and institutions. A systemic risk EWS is only one of
the tools capable of this role. Y et, it enables a distinct set of policy applications.

This study shows that a systemic risk EWS provides a consistent conceptual basis for the
deployment of macroprudential policy applications as a function of systemic stress. It extends the
topic of EWS supervisory policy applications, up to now insufficiently developed. This basis
further substantiates macroprudential policy choices in contrast to the conventions of
microprudential practices.

Strategically, systemic risk EWS focuses on identification of stress and institutional
imbalances, in addition to forward looking analytics, differentiation of excessive exposures,
sensitivity to systemic risk posed, and capacity for macroprudential risk management. Dealing
with stress aggregation across time, potential EWS policy applications in pursuit of two tasks are
discussed: prevention and mitigation. One of the principal EWS benefits in this context is
discriminating imbalances that have strong positive and negative associations with financial
stress. This differentiation allows a rich set of policy applications including use of defensive
imbalances as stress buffers, limit setting on common adverse imbalances, and institutional
targets for imbalance diversification.

Notwithstanding the potential for these powerful applications, this study also urges two
notes of caution. First, care must be taken in the calibration of macroprudential applications,
given their reliance on quality of the underlying systemic risk-modeling framework. Second,
macroprudential applications should not commence without explicit economic impact analysis of
feedback mechanisms involving the new policies.

Overall, the paper explores macroprudential applications for systemic risk in a dynamic
institutional context. A ppropriate strategies and instruments ground on identifying and disclosing

95 Acharya (2011), pp. 26-27.

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overall stress based on a systemic risk EWS. While this new direction of supervisory
applications targets to enhance financial system transparency and strengthen the resilience of
infrastructure and institutions, the feedback interaction of policies and the financial system
agents also has some adverse potential. Therefore, the dynamic effects of macroprudential
applications should be well considered in advance.

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TABLES
Table 1

Macroprudential tools in use among respondents to the 2011 IMF monetary and capital markets

department's “financial stability and macroprudential policy survey.” Source: Lim et al. (IMF

2011a).

% of respondents

Early warning models

% of Advanced economies _% of Emerging economies
42.9

Asset price/real estate valuation models 41.2 56.5 28.6
Single-institution models 54.9 73.9 39.3
Systematic financial sector risk models 33.3 52.2 17.9
Contagion risk models 39.2 39.1 39.3
Macro-financial linkage models 35.3 43.5 28.6
Stress test 39.2 43.5 35.7
Table 2
Systemic feedbacks typology
Class Subclass
Structural T Composition = Connectivity
+ Concentration
= Correlation
2 Regulation = Incentive regulation
— Structural regulation
—___Ex-post crisis intervention
Interconnective 3 Assets and Liabilities transformation - Balance sheet
= ___A/L maturity mismatch
4 Credit =__ Credit chains
5 Liquidity transformation = Fire sale
— _ Liqadverse selection
= Liquidity hoarding
6 FX = ___ Exchange Rate
Behavioral 7 Uncertainty - Knightian
= Cognitive bias
8 Information = Asymmetry
= Spillovers
— Sensitivity
= Herding

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Table 3
Macroprudential strategies of prevention (stable, ex-ante, and ex-post phases) and mitigation
(critical phase)

[Authors Removed] (2011 Frait and Komarkova_ BOE (2011) - and Wierts ‘Sinha (2011) -
SAFE) - (2011) - CNB® BOE (2011) - DNB RBI
Objectives RBC
Model Basis: EWS SAFE] Model Bass — Model Bass — Model Basis — Model Basis —
Til Short-term: iit 1, eniaton of sess limits 4s limits
5 hes system: ee formontoring 2 COuntereyclical margins 2, distribution restrictions
2 3, Suess leveland rate of 3-assetate rules 3. margining requirements 1. time-varying limits Lcountereyctial mits
Be ange monitoring SGhcvanlng pevsering  Sumeroning burs 2 country uters 2 countereycialprovslring
£ T2[Long-term: aie 5. time-varying provisioning 5. time-varying b iat 3 counrejekesl tek wale
avoid - S.sme-saryng reserve 6. time-varying provisioning nyieg ma
Ect | TRS, require 4 tmesvaryingfisk weights
Parnas (nn. Tmevening rskwelgts a. dscosue
financial instability
X1.1] Common
exposures 1. monitoring  identfcation of
imbalances: mit common and adverse cross
severity of failure sectonalong run imbalances. 1 time-varying surcharges
2 limits on commen and. limits ate 2. time-varying limits 1 countereyclical limits
z verse cross-sectional long 3 disclosure 2 2,
§ —X12|Common tun imbalances 4, communication
& imbalances: limit 4. communication
2 & probability of failure
22
aE 1 encourage idsyneatc
Bf imbalanc

2: progressive reduction in

‘required minimum buffers and

targets given idiosyncratic vanibginikirs
x2 imbalances Piime varying butt

| Imbalances sefensive 2. tme-varying margins
Interconnectedness: 3,¢n¢ouras 5. time-varying targets 1. central counterparties
S strengthening ene. awa 4 3. counteryclca isk weights,
infrastructure requirements 3. disclosure

defensive roe rai
yesiience 5. progressive reduction ing SOS

required buffers and limits active A communication

wen sions exceed

defensive

1d

8, communication

7, dentiicaton of stress __ 1 release of buffers
Tec phase 2 capital injections 2 limits
nancial sy BMS ccc tormenminy 3 deena 2 disbibution esticions
inde Sslovelangvetof” 4. communteaton Zemaginng requirements 4 me-vaying ins 1.counereyeal iis
T2] Long-term: ‘change monitoring rf
mai Snesang ts «cl Shera ie a a eee

toes yen ene isclosure ime-varying provisioning
macroeconomics ae 2 cemmtisiesiion. 7. time-varying risk weights
costs linke: 7 8 disclosure
financial instability Commuricaton
XL1|Common 2 Moning /ideniaton oF

« S-

Sectional shiterun imbalances varying limi ountereyclical limi
fibotonees: mt Sectonel shun mbal 1. time-varying limits 2 countereyclical mits
severity of failure adverse cross-sectional short 1. disclosure eros
Sao common —— tin imbalances 2 communication

z X12] common pail

& exposure é

Ee imbalances: limit * ¢o™munication 2 2
o probability of failure

5 TERETE TRGTTATE

imbalances

2 progressive reduction in

‘required minimum buffers and

targets given idiosyncratic

Lae) " 1. central bank refinancing

imbalances
x2] 3. 2.

7 3: deposit insurance 1. central counterparties

imbalances
strengthening eet aed bng.nin 4 contingency funding 3. countereyclcal isk weights

© infrastructure defensive targets 5. living wills 3. disclosure
resilience 5. progressive reductior 6. disclosure
7: communication

‘required buffers and limits
when institutions exceed

8. communication

56 Omitting suggested monetary and fiscal policy tools

-38-

Table 4
Macroprudential instruments of prevention (stable, ex-ante, and ex-post phases)

PREVENTION

Object Thathors Removed] (O11 SAFE)-Fraitand Komarovd 2011] BOE OTH ‘Schoenmakerand Sinha (2011)
eves. Wierts (2011) - DNB RBI

Model Basis: EWS (SAFE) Model basis— Model le Model basis— Model basis—

T dencaton of tess phase TGmevarance montomng 1. ims
credit spreads and risk loan-to-value ratio uv general credit growth

stress remia tosincome ratio. leverage ectors by growth and
T1|Shortterm: limit _ stress grate threshold market liquid / mover pric

stress le financial stability indicator repo rate

financial system-wide

RE risk-weight
2 ume ii

om oe por eet Chane reditto GDP imbalance set cl reverse repo rte
od tate of change asset imbolance funding tines ening change cash reserve rato
2. guideines for mentoring asset 4 imbalance funding instrument Tigudiy charge Henle LV by
shotin mblncee ioan a inbala Cistrbution restictons varying NSFR ‘ght
tansiton 2. ints (household sector fixedNariable dividends varying LCR 2 countercycicl
ses level and rate of change vassal finedvarable b provisioning
time-varying mits debtto.ncome ro fixedNarable erp general cred gow
iauay index interesttpsncome rato onuses rs by growth and
priceso-income reso. 3: margining requirements
iran concertos foeno-vale eto capa
priceto-ent rat haitets on secured exposes housing
4 merarging tangets 3. ins nancial sector francing and dervatve
solvency evel "ab tansectons fous
sovency dance ‘A.coumtereycical margins 4 countereylial bufers
5 ial funding haircuts “api Ce
‘ ge 5. assetrate re figuity NBFC exposure
E k cul [: feren 5. time-varying buffers 3. countercyclical risk
z interest rate hedging 6. countereytial buts to weahts
= interest rate distance capital adequacy ratio scalar over LCR /LAR gener ces.
insttutoneliauity 7. varying provsionng stress fr LER /LAR growth and
E 1a|Longterm: avoid fuity bute ioamoss prowsion rate scalar over NFSR [CFR pce
macroeconomic costs credit risk distance coverage rat A stress for NFSR /CFR Eapial m market
linked to financial 5. disclost default rate ible ste test xposures housing
insta a stess Wentication measures NPL rate toans
stress grade, suess phase, suess increased colater 6. tinesvaryng provisioning nomhousing real
ievel suess rte of cha varying reserve requirements "buffer over ace, provision toons
ope gues fe requiements fule-bosed capital reserve ERE loans
mits, shortun 9. varying isk wetghts sectoaggregate butler NBFC exposure
inosarces weights
fong-tem target ERE loans Sectoral capital bufer
cask warnings Fx tans sectoral ik weights
puble imbalances: short and lon, scalar of exposure
publ forecasts: short and on 2. disclosure
a stesst soveregn sector
6. communica Banking sector
2. private imbolonces spect sks
B, macroprudentia its
€. macroprudental targets
dk svess test
toning /sdentReaBon of common
Cross-sectional long run imbalances capital Calibrated coptal charge LTV general credit growth
X1.1|Common —_S"imitat common tross-sectonaliong __iquidity Callated iguidty charge leverage Sectors by growth and
‘exposures run imbe 2. limits calibrated ada'tl RE risk-weight price
imbalances: mit 5 Gscosure (see abo Toans-t-deposts rato insta repo rate
severity of failure communication (see above) interbank funds ratio 2. disclosure reverse repo rate
Lmistatch ebo soverelgn sector ash esene ro
Toning Ea aTaaRTETE Hed mfelance tao seca eae IN ene
adverse long run i iquidty imbalance tests
Smit on aggregate adverse ng on "aut soc
imbalances aciay sc
5 disclosure (see above
Shae of rae 86 Sis
4 communication (see above) ee
Capital qualiy stucture
fre debe os
curency:9

Cross-sectional dimension

X1.2| Common
‘exposure imbalances:
limit probably of
failure

curency Tans share

3
3

sgovmnt deficit imbalance

serves
extemal financing reqs
currency valuation

Ti engmupae le ebabnces
time.

i

funding hares

2. tne sary utr 2. time varying
Renuatedesveimooaness "eben ‘tomethost)
capital qui
x21 figuty 3. tmevalying gets
interbank
Curency exposures reduirements
strengthening icuatydeance stestering —S-doure
infrastructure interest rate hedges ictive A communication 3.
resilience credit risk distance stress-testinc
4. customized lng run defensive targets
‘andardanaidozyerie
ures by inst
5, discosure (see above
é above)

3
tool to mandate CCP use SIFI capital surcharge

2. wading circuit breakers

defined class trading
venue

defined class market
make

defined class circuit
ker

specific risks

3 countereyeleal rk
weights
general credit growth

sectors by growth and

price
Capital market
exposures housing

loans
rnon-housing retall
loans

RE loans
NBFC exposure

-39-

Table 5
Macroprudential instruments of mitigation (critical phase)

TAuthors Removed] (2011 SAFE)- — Fraitand Komarkova BOE (2011) - ‘hoenmaker and Wierts Sinha (2011) -
Objectives FRAC = CNB BOE (2011) - DNB Ral
Model Basis: EWS (SAFE) Model Basis — Model Basis — Model Basis — Model Basis —
T Wentcation of Stess phase 1. Telease of buffers Tis Te wvarjing mis I countercyclcal mis
ess components Brovsionng (coverage "Teannaalue ati uv general credit growth
stress i, LLPR loanto-income ratio leverage Sec nth
Stress grade threshold 2. captalfectons leverage ratio Re rskweight price
stress level 3 disclosure sectoral iquiity 2. countereyclial buffers 0 rate
ane period rate of change profitability sset class capital revere repo
0 period rate of change NPL ratio nding sou >. netaning charge cash rese
2. guidelines for monitoring ge in CAR funding instrument ius cage fenble LTV byrsk
imbalanc 4. communication 2. distribution estctions SFR weight
transition matices credit spread Tnesharabiedvidends varymgcr 2. countercyclical
sess evel and rate of change ets fixedvarable buybacks provisioning
3. time-varying limi Creditrsks stress test __fxedNariable empl ‘general credit growth
quit index onuses
EDF 3. margining requirements, price
terbank concenatons static / time-varying Capital market exposures
rage haircuts on secured using

T1|Shortterm: limit 4 ae waiving angels financing and derivative
‘nance system-wide $2

jans
Sclereyditance ccountercyclical buffers

capital ber capital
leverage liquidity
interbank currency 5. time-varying buffers

interest rate hedging
interest rate distance
institutional liquidity

leverage ratio

Time dimension

liquidity bute scalar over NFSR (CF!
credit risk distance ‘A stress for NFSR /CFR
5. disclosure individual stress test

‘a, stress identification measures
stress grade, stress phase,
stress level. stress rate
change

». prudential guidelines

rm limits, short run

time-varying provisioning
ue aie ep
rulebased

secovaggregatebuter

imbalances, transition time-varying risk weights
matrices, long-term targets I capital buffer

cc. risk wamings sectoral isk weights
public imbalances: short and scalar of exposure
long, public forecasts: short disclosure

2| Long-term: avoid
macroeconomic costs
linked to financial
instability

and long

d. stress-testing

communication

2. private imbalances
‘macroprudential limits

6 specific risks

nom hcosing Fetal ans
E loans

NBFC exposure
3. countreylca sk
Sights

“ete credit growth
rowth and

wee”

pita market exposures

housing

non housing retlloans
RE loan:

NBFC exposure

Zz . macroprudential targets

iy dus ting

§ T. monitoring Fidentficaton of 1. disclosure T dacosure

g ‘ommon cross-sectional short run ‘in mi auiity sovereign sector
imbalances banking sector

= XL Commen 2rls on common crse-secional _ereank spreads specific risks

shi {gov bond spreads
3. disclosure (see above)

severity of failure 4, communication (see above)

spreads
joint probability of
ss

1. monitoring identification of Naqulaty Stress test

Time-varying ims
Lv general credit growth

leverage Sectors by growth and
RE risk-weight price
repo

flexible LTV by risk
weight

2. countercyclical butlers 2. countercyclcal
«:

aggregate adverse short un interbank contagion test pha provsonin
imbalances Cova general credit growth
xa2|Common 2 lnis on apse advene stor SOA a. sectors by growth an
sure Inbalances : disclosure (see above) capital market
limit probability of 4° communication (see above) exposures housing
¢ fai ans
5 non-housing retail loans
2 CRE loan
E NBFC exposure
bs 7, encourg 5, cental bank rei F 5
2 imbalances 4:qovernmentguarantees _"toolto mandate CCP use SIFI capital sucharge weights
2 5 ji 2, stuctural ener crest
| Arges educlonin 6: coningency indng defined class wading improvements rs by growth and
; ‘equired minimum buffers and ven Tromargmshaicuts pri
H tgs avn dosyncrate defined class market. defined class trading” Capital m
§ imbalan maker von haus

2. encourage defensive Inbalances

defined class circuit
varying risk weights breaker

x2] capital 3. disclosure
Interconnectedness: liquidity sovereign sector
strengthening interest rate hedges banking sector
infrastructure Credit risk distance stress-testing specific risks
resilience liquidity distance stress-testing

4, customized long run defensive
targets
‘standard and idiosyncratic
exposures by institution
5. progressive reduction in required
buffers and limits when institutions
exceed defensive
7. disclosure (see above)
8 above)

loans
ronhousing retal oars
CRE loans

NBFC exposure

-40-

Table 6

Probability of systemic stress episode by CFSI grade
CFS!

Probability of systemic stress

rating grades Ranget at grade threshold
Grade 1 (expansion period) Zersi< -0.50 1.9%
Grade 2 (normal period) -0.50 <Zersi< 0.59 8.7%
Grade 3 (moderate stress period) 0.59 S$ Zcrs) < 1.68 26.3%
Grade 4 (significant stress period) Zersi2 1.68 53.3%

¥ Note: Range analysis is performed on CFSI standardized distances (z-scores)

-41-

Table 7
Benchmark and base models out-of-sample static forecasts

nel Ai
Benchmark FSI 7.85 + 0.60FSI_; + 0.24FSI_¢
model DF=58 K=2

RMSE” MAPE Theil U
8.35 1242 0.081
20 ss
‘jog ised yee i988 Se0d 3003 Sood Seed b00d Seid
pene Be FSI = 36.58 + 0.35FSI_, + 1.70GT_AL3_5 + 7.04GT_LEVN_o + 2.344PMKTCP_« — 12.62ACRCAP_NV_13
base Model DF=61 KS
Ta5
RMSE © MAPE— Theil)
17015242
°
jops" bed YesdYeed b00d Sood dood Seed Boos b0i8
Fanelc

ch FSI = 38.77 + OAOFSIy + 2.06AHFX4.¢ + 8.65AHEQS.9 + 8.15GT_LEVN_s — 2.944EQLGDW3_, ~ 4.55CR_EVSV_g
jortlag base DEeeI ia}

169

aao

RMSE  MAPE Theil

a20 |
200 |

ao |

9.04 11.83 0.084

Tosd "i996 199d "deed Boos 300d” Be0d Dodd 301d

fate FSI = 37.85 — 9.88GT_ALG3_o + 2.29EDF_4; — 2.24CR_EVNV_¢ + 4.55GT_HIB_g + 11.20GT_LEVN_,
ong-lag base ape pee

60

1ao

RMSE MAPE Theil U
120 4

15.14 18.75 0.143

yese tesa” Beed”Be03”Be0a Be0a” Beed” 2ei0

Note that the RMSE errors may not be directly compared across these models due to the differences in their
forecasting horizons. The benchmark FSI model forecasts one quarter ahead, candidate base and short-lag base
models forecast two quarters ahead, while the long-lag base model forecasts six quarters ahead.

-42-

Table 8
Sample transition matrix (leverage).

Leverage change (std)
d

Grade 1 Grade 2
Grade 1 X12 =3.5
Grade 2 =
Grade 3
Grade 4

x 2)
‘Note: Xi denotes the change in imbalance, measured in Sanda Savatons, That is associated
with transition of stress from grade i to grade j.

-43-

FIGURES

Identficaton of systemic conditions

1 policy Forward-looking

|
. ‘bectves
|

instability

Forecasting

FUNCTIONS
‘of paicytools
(Gonzalez Herrsilo, 1996; De Baret ana Hartmann, 2000;

Boro, 2008),

IN

X12] Common exposures imbalances: Imitseverty of failure
Identification of systemic imbalances

Sensitivity to systemic isk posed

‘ADAPTIVE FINANCIAL SYSTEM
(Holland, 1975, 1988; Aghion and Howitt, 1992;
Farmer, 1990; Arthur etal., 1997; Brock and
Hommes, 1997, 1998; Hommes, 2001; Brock and
Durlauf, 2001, Hollingsworth et al., 2005; Farmer,
(002; Farmer et al., 2005; Howitt et al., 2008;
Bech et al. 2007a, 2007b, 2010)

Expected lass calculations Early pele dane
fm

Local robustness analysis

Robustness with muliple models

FORMS
‘ofrracroprudential tools

(Limetal., 20112, 2011b;

Galati and Moessner, 2012)

Stress testing

EVALUATION
‘of policy toals
(Lucas, 1976; Satter, 1991: Brock et al.,2003)

Fig. 1. Conceptual Model: Early Warning Policy Use in A daptive Financial System.

44.

°%) Utilization of Quantitative models/tools

g — Risk identification

—— Risk assessment

—— Resilience of the financial system

valuation models
Single-institution risk
models

sector risk

cross-country network

models
Macro-financial linkages
models

Asset price/real estate

Early warning models of
financial crises
model:
Contagion risk models and

Note: vertical axis measures percentage of respondents to the 2011 IMF monetary and capital
markets department's “financial stability and macroprudential policy survey.

Fig. 2. Utilization of macroprudential tools. Source: Lim et al. (IMF 201 1a).

Stability Ex-ante Crisis Ex-post

Fig. 3. Conceptual time phases of systemic financial stress

-45-

Fig. 4. Cleveland Financial Stress Index

1% Critical Value

= ) 5% Critical Value

W\
oe

2012

R

Grade 4

== Actual CFSI
~-@--- Long Lag Forecast
— — Short Lag Forecast

Grade 3

Grade 2

Grade 1

2Q 2007
2010 2011 2012
Grade 4

2

2007 2008 2009
Fig. 6. Out-of-sample SAFE EWS forecast as of 2Q 2007.
3

Grade 3

Grade 2

NY vay

mya TP -

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

2 :
Fig. 7. Signaling rule alerts to significant developing stress.


linahcial re
trem Crigis i ke

mii) MY

vooz | S

== 2 quarter change 1 quarter change
Fig. 8. FSI, and FSI, alerts to significant Fig. 9. FSI alerts to significant developing stress.
developing stress.
Short Lag Long Lag GLoan portfolio (cpi)
R ivati
Ginterbank currency (cpi)
time Binterbank currency (ta)
(quarters) interest rate derivatives

BAL gap: 0-3 months
GAL gap: 3-12 months
Lig index: 1 yr fwd

6 GIRR through the cycle

GIR capital through the cycle

GIR capital in stress

GIR capital in crisis

GCR capital through cycle

GEDF

Economic value of loans (BankCar)
Solvency through the cycle
Solvency in stress

Solvency in crisis

GIR distance to crisis

GIR distance to stress

GCR distance stress to crisis

GCR distance to stress
@Solvency distance stress to crisis
Solvency distance to crisis
Connectivity CoVaR

@Currency mkts concentration
GFX mkts concentration
Ginterbank concentration

Beverage

“12
Fig. 10. Institutional imbalances’ Granger contribution to stress.

-48-

Target

Units of CFS! m1Q ahead m2Qahead =3Qahead m4Q ahead =5Qahead =6Q ahead = — — ~Limit
5

7
=F

EDF

Loans (cpi)
Solvency

Liquidity index - stress
Liquidity Index - fire sale
Interbank (cpi)
Securitizations (cpi)
‘Securitizations (ta)

IR derivatives (ta)
Solvency in stress

IR distance to crisis

CR distance to crisis
Solvency distance to crisis,
Interbank concentration
FX concentration
Leverage

Note: The figure describes sample long-lag contributions of a subset of the top twenty five bank holding companies as of 1Q 2012.
Fig. 11. Potential targets and limits through monitoring of imbalance percentage contribution to stress.

Equity Markets
Credit Markets 0

FX Markets
Interest Rate Derivative Markets a

Currency Markets
Credit Derivative Markets
Interbank Markets

Securitization Markets

Fig. 12. Risk topography of financial market concentrations of top 25 US BHCs across markets and time.

of top five US BHCs: 1991-2011.

)

Fig. 13. Individual contribution to systemic financial stress (CFSI

3
Z
3
2
5
2
&
=

Concentration -
Currency Market
(interbank)
Microprudential
Credit Risk - normal
distance-to-systemic

Fig. 14. Sample of imbalances by an individual financial institution: 1991-2011

-50-