Systemic Risk Monitoring ("SysMo") Toolkit—A User Guide

There has recently been a proliferation of new quantitative tools as part of various initiatives to improve the monitoring of systemic risk. The "SysMo" project takes stock of the current toolkit used at the IMF for this purpose. It offers detailed and practical guidance on the use of current systemic risk monitoring tools on the basis of six key questions policymakers are likely to ask. It provides "how-to" guidance to select and interpret monitoring tools; a continuously updated inventory of key categories of tools ("Tools Binder"); and suggestions on how to operationalize systemic risk monitoring, including through a systemic risk "Dashboard." In doing so, the project cuts across various country-specific circumstances and makes a preliminary assessment of the adequacy and limitations of the current toolkit.

Abstract

There has recently been a proliferation of new quantitative tools as part of various initiatives to improve the monitoring of systemic risk. The "SysMo" project takes stock of the current toolkit used at the IMF for this purpose. It offers detailed and practical guidance on the use of current systemic risk monitoring tools on the basis of six key questions policymakers are likely to ask. It provides "how-to" guidance to select and interpret monitoring tools; a continuously updated inventory of key categories of tools ("Tools Binder"); and suggestions on how to operationalize systemic risk monitoring, including through a systemic risk "Dashboard." In doing so, the project cuts across various country-specific circumstances and makes a preliminary assessment of the adequacy and limitations of the current toolkit.

I. Introduction1

1. Macroprudential policymakers need to know when to act. Policies to mitigate system-wide risks should be based on detailed information on where and when such risks are building up and which channels may amplify their impact on the broader economy.

2. This paper aims to clarify the nature and use of the systemic risk monitoring tools that are currently available. Building on earlier surveys,2 it looks at all dimensions of systemic risk and assesses the tools’ ability to capture these dimensions. The paper offers suggestions on how to use the tools, taking into account their nature, focus, and relative merits and limitations. It also focuses on the systemic risk signals, including their timeliness, the types of risks they cover, and ways of interpreting them. However, this paper does not analyze the direct relevance of specific systemic risk measures for the selection of appropriate macro-prudential policy tools (and their calibration).

3. This paper offers guidance on how to select the best set of available tools under various circumstances. Effective risk monitoring should be based on a clear understanding that: (i) policymakers should not expect to find “all-in-one” tools, because the reliability of systemic risk monitoring tools depends on the circumstances in which they are used; and (ii) policymakers should take into account several potential sources of risk by using a range of tools at any point in time. Against this background, the objective of this paper is to identify those tools (or combinations of tools) that are most effective in measuring a specific dimension of systemic risk. It provides policymakers with some general principles based on cross-country analyses, but it also encourages practitioners to calibrate the toolbox in view of country-specific circumstances.

4. The structure of this guide follows a practical approach. After a brief introduction to systemic risk and the key features of the existing toolkit, the guide discusses a range of systemic risk monitoring tools. They include, for example, tools focusing on a narrow (but potentially systemically relevant) sectoral perspective, as well as tools to measure the risk of a systemic crisis. There are four complementary ways to access and use this guide (Figure 1):

  • An in-depth discussion of six key questions on systemic risk that policymakers are likely to ask (Figure 1): Is potentially excessive risk building up in financial institutions? Are asset prices growing too fast? How much is the sovereign risk a source of systemic risk? What are the amplification channels among sectors and through the broader domestic economy? What are the amplification channels through cross-border spillovers? What is the probability of a systemic crisis? In addressing each question, the emphasis is put on combinations of relevant tools in light of their relative merits and complementarities.

  • A living inventory (“Tools Binder”) that offers a two-page snapshot of each tool, summarizing its key properties (methodology, coverage, interpretation, data requirements, etc) and providing a concrete example of its use.

  • A sample systemic risk Dashboard for a fictitious advanced country that illustrates how, in a specific country context, various complementary tools can be combined to monitor key sources of systemic risk.

  • Tool selection tables that summarize which tools are available for which purpose and country category, thereby helping users to readily identify the most relevant tools.

Figure 1.
Figure 1.

Structure of the Guide

Citation: IMF Working Papers 2013, 168; 10.5089/9781484383438.001.A001

5. Finally, the paper concludes by highlighting how well the various dimensions of systemic risk are covered by the current toolkit, and by identifying some key analytical gaps that could benefit from future research.

II. Approaching Systemic Risk

A. What is Systemic Risk?

6. Lessons from past and current crises highlight key sources of systemic risk, the evolution of these risks over time, and the underlying macro-financial linkages:

Definition. There is an evolving literature on systemic risk measurement covering a wide range of approaches. In the context of this paper, systemic risk is defined as risk that originates within, or spreads through, the financial sector (e.g., due to insufficient solvency or liquidity buffers in financial institutions), with the potential for severe adverse effects on financial intermediation and real output. The objective of macroprudential policy is, therefore, to limit system-wide financial risk (IMF, 2011a) by enabling policymakers to know better when to “sound the alarm” and implement policy responses.

Phases. Past crisis episodes show that different sources of risk and shock transmission channels can emerge at the same time or in complex sequences, including through multiple feedback effects. However, from an analytical perspective, it may be useful to distinguish between key phases in which crisis-related events unfold. At the same time, policymakers should be cognizant of macro-financial linkages during each phase. Ultimately, most systemic crises involve feedback effects between the real economy and the financial sector, including across countries.

Theoretical and empirical models dealing with interactions between the financial sector and the real economy, as well as between cross-border transmission channels, are useful for monitoring purposes in general.

  • Buildup phase. Systemic risk builds up over time, and this could reflect several underlying reasons. The financial system may have high exposure to an overheating sector, or be subject to increased risk-taking (e.g., due to competition for market-share or lax supervision), including through financial innovation. The risk buildup could also be related to growing cross-border exposures and funding sources. During this phase, systemic risk measures could focus on assessing the likelihood of a systemic crisis (Figure 2), taking into account the evolving balance between potential financial losses and existing buffers designed to absorb these losses.

  • Shock materialization. At that point, the crisis is about to start. Mounting imbalances or excessive risk-taking make the financial system fragile and susceptible to exogenous shocks (e.g., GDP or fiscal shocks, exchange rate or housing price shock, failure of a systemically important financial institution). Therefore, systemic risk measurement could focus primarily on assessing potential losses in both the financial system and the real sector.

  • Amplification and propagation. In most crises, shocks affect the broader system, including financial institutions, markets, and other sectors (and potentially other countries’ financial systems). At that point, systemic risk measurement could focus on amplification mechanisms, such as interconnections between financial institutions, potential fire sales of financial assets, as well as crossborder exposures and the related adverse feedback loops (Figure 3).

Figure 2.
Figure 2.

Buildup of Systemic Risk: Sources and Channels

Citation: IMF Working Papers 2013, 168; 10.5089/9781484383438.001.A001

Note: FSI stands for Financial Soundness Indicators; T-model: Threshold Model; DSA: Debt Sustainability Analysis; CCA: Contingent Claims Analysis; BSA: Balance Sheet Approach.
Figure 3.
Figure 3.

Unwinding of Systemic Risk: Sources and Channels

Citation: IMF Working Papers 2013, 168; 10.5089/9781484383438.001.A001

Legend: MR: market risk; IR: interest rate risk; CR: credit risk; DSA: debt sustainability analysis; S/T: stress testing; CCA: Contingent Claims Analysis; SysCCA: Systemic CCA; FSI: Financial Soundnes indicators; JPoD: Joint probability of Default; EVT: Extreme Value Theory

Measurement challenges. During the recent global financial crisis, various shock transmission channels reached an unprecedented level of complexity. For example, the range of potential shock transmission channels has broadened considerably, reflecting the greater integration between financial institutions and markets, countries and real sectors (e.g., linkages between public and financial; household or corporate and financial; public and external). As a result, macro-financial linkages and systemic risk are more difficult to measure, given the potential for more complex and unpredictable scenarios, greater scope for nonlinear impacts (e.g., through illiquid markets or institutions), and more unstable correlation structures and behavioral relationships.

B. Key Features of the Toolkit

7. Focusing on risks at “various” levels. Available tools may be used to measure systemic risk at different levels of aggregation, including:

  • Individual financial institutions and markets. For instance, these include (i) market valuation tools to identify price deviations from trend or from levels implied by fundamentals, focusing on assets that are relevant to financial stability (e.g. housing, equity or bond markets); (ii) indicators of risk-taking and stress testing tools to assess the resilience of financial institutions or sovereigns.

  • Risk transmission channels. Models measuring interactions among financial entities have evolved rapidly in recent years. They are designed to better capture time-varying and nonlinear distress dependences (e.g., during extreme events), or the marginal contributions of individual institutions to systemic risk.

  • The whole financial system and the economy. Crisis prediction and stress test models aim to capture the risk that the entire financial system is impaired, as well as macro-financial linkages and feedback effects with the real economy. Also, general equilibrium models increasingly integrate financial sector and macroeconomic variables.

8. Types of risk. What are the most relevant types of risk that should be monitored and mitigated during each systemic risk phase?

  • Credit risk. This is a key source of risk in most financial systems. Stress testing methodologies, in particular, have relied on increasingly sophisticated approaches to assess probabilities of default and potential losses if default were to occur (loss-given-default or LGD), especially in relation to various macro factors.

  • Liquidity risk. Liquidity risk measurement tools have recently been developed to assess not only potential changes to financial institutions’ liquidity ratios, but also the interactions between market liquidity (e.g., for thinly traded, illiquid assets) and financial institutions’ funding conditions (e.g., through collateralization channels).

  • Market risk. There is greater familiarity of financial institutions and supervisory authorities with assessing such risks, including through stress testing for interest rate, exchange rate, or asset price shocks. At the systemic level, aggregate measures of market volatility can be used to assess latent vulnerabilities (e.g., to identify periods in which markets are more likely to become more volatile).

9. Underlying methodology. Depending on country-specific circumstances, various types of tools and underlying approaches or methodologies are available:

  • Single risk/soundness indicators. Indicators based on balance sheet data, such as financial soundness indicators (FSIs), are widely available and cover many risk dimensions. However, they tend to be backward-looking and do not account for probabilities of default or correlation structures. Moreover, only some of these indicators can be used as early-warning tools (e.g., indicators of funding structures). Market data can be used to construct complementary indicators for higher-frequency risk monitoring.

  • Fundamentals-based models rely on macroeconomic or balance sheet data to help assess macro-financial linkages (e.g., macro stress testing or network models). By providing vulnerability measures based on actual interconnectedness and exposures, these models may help build a realistic “story.” However, they often require long-term data series, assume that parameters and relationships are stable under stressed conditions, and only produce low-frequency risk estimates.

  • Market-based models. These models uncover information about risks from high-frequency market data and are thus suitable for tracking rapidly-changing conditions of a firm or sector. These approaches are more dynamic, but their capacity to reliably predict financial stress has yet to be firmly established.

  • Hybrid, structural models. These models estimate the impact of shocks on key financial and real variables (e.g., default probabilities, or credit growth) by integrating balance sheet data and market prices. Examples include the CCA and distance-to-default measures, which compare the market value of an entity’s assets to its debt obligations.

10. Toolkit limitations. As highlighted above, available tools are very heterogeneous: none is universally applicable to address all aspects of systemic risk, and all are subject to important underlying assumptions, data issues, or “model risk.” For instance, as is widely acknowledged, the informational content of market prices may be undermined under certain circumstances (e.g., both during stress and “exuberant” times) or may not capture rising interconnectedness within the financial system. More broadly, and despite ongoing progress in developing and improving the toolkit, efforts to integrate individual tools into a comprehensive and internally-consistent quantitative framework (e.g., across sectors, types of risk, or time horizons) are still in their infancy.

III. Mapping Tools to the Territory—A Practical Approach

11. This section presents the existing toolkit by addressing six key questions policymakers should ask themselves as they assess systemic risk. Building on the “Binder” presented in the Appendix, which presents each tool separately, the focus of this section is on the best selections and combinations of tools to address each key question, taking into account the complementarities among tools and their relative strengths and weaknesses.

12. The proposed sequence of key questions broadly reflects the increasing extent of macro-financial linkages involved in systemic risk monitoring. Specifically, and for practical purposes, the assumption is that policymakers would start from a ‘funnel-view’ of the economy, looking at (i) narrow sources of risk within the financial sector (e.g., financial institutions or asset markets), and then turning to (ii) other sources of systemic risks or risk amplification (i.e., in other sectors, the broader economy, or other countries), and finally (iii) aiming to directly measure the risk and probability of systemic events. In addition to better understanding the underlying sources and severity of crisis risks, such a structured approach may also help policymakers to mitigate systemic risk more effectively, including through a tailored use of specific macroprudential policy tools (IMF, 2011b).

A. Financial Institutions: Is Potentially Excessive Risk Building Up in Financial Institutions?

13. In order to gauge risk buildup at the aggregate level, one should use a combination of balance sheet data that indicate whether financial institutions are taking increasing risk, with potentially systemic impact. Financial Soundness Indicators (FSIs) provide a starting point, as they focus primarily on aggregate balance sheet soundness, and may help to identify sources of risk buildup (e.g., FSIs related to sectoral credit growth and leverage).

14. FSIs are collected comprehensively for many countries and cover a broad range of key risks and buffers, but they tend to be backward-looking indicators. A similar set of indicators is provided by Bank Health Assessment Tool (HEAT), which builds on CAMELS-type financial ratios to derive individual bank indices and can be used to monitor aggregate banking soundness.3

15. Complementing FSIs, Market-Based Probability of Default measures such as Distance-to-Default (DtD) or Expected Default Frequency (EDF) can be used to assess with higher frequency the probability that individual financial institutions may undergo distress or fail (where relevant market prices—such as equity or CDS prices—are available).

16. Macro Stress Tests can be used to examine more closely the sources of financial institution vulnerability and to identify specific weak links in the system. Macro stress tests capture a range of risks (e.g., credit, liquidity, and market risks) under “extreme but plausible” (i.e., tail risk) adverse scenarios. They combine these risk factors to evaluate whether financial institutions (both in aggregate and taken individually) have enough capital and liquidity buffers to withstand such scenarios. Key challenges in using stress test models include the calibration of appropriate and internally consistent sets of shocks (across risk factors), and incorporating feedback effects from financial sector problems back into the macroeconomy.

17. From a more aggregate and forward-looking perspective, credit growth is often central to the buildup of macro-financial risk, and models such as the Thresholds Model (or T-model) provide rules of thumb on thresholds for changes in credit-to-GDP and its deviation from trend that may signal a systemic financial crisis. However, the T-model tends to produce thresholds that are fairly low in order not to miss a crisis, and should thus ideally be combined with other tools that tend to yield higher thresholds (e.g., Dell’Ariccia et al, 2012) so as to reduce the chance of a false signal that might lead to a costly policy mistake.

18. Finally, a number of tools focus on interdependences between financial institutions and assess the risk of spillovers among them. In doing so, these tools may also allow practitioners to identify systemically important institutions. Ideally, policy makers have data on actual interlinkages between financial institutions and systems. In that case, Network models can be used to gauge such spillovers triggered by shocks in any one, or more, financial institutions (e.g., the ‘weak links’ identified above). Such tools can also be applied to aggregate data on cross-country exposures to gauge cross-border spillover risks among financial systems (e.g., based on BIS data). These models provide information on potential spillovers through direct exposures. But they do not offer information on how the system might behave during crises, when both direct and indirect (e.g., common) exposures come into play.

19. Complementing the above analyses (or replacing them in the absence of data on direct exposures), models based on market data allow for high-frequency monitoring of the likelihood of spillovers between financial institutions and systemic stress within a short-term horizon (typically less than a year, i.e., near or during crises). They include Joint Distress Indicators (JDI)/Financial Institutions Stability Index (FISI), Volatility Spillovers (Diebold-Yilmaz (DY)), CoVaR, Distress Spillovers (DS), Systemic CCA (SCCA). These models and indicators can be used to assess spillovers either under normal (DY) or extreme conditions (JDI, CoVaR, DS, Systemic CCA). Moreover, the Systemic Liquidity Risk Indicator (SLRI) provides a coincident indicator of systemic liquidity shortages during market distress. These models do not trace back to the specific risk channels through which such spillovers occur, but some of them help identify which institutions are more systemically important (by estimating individual contributions to systemic stress).

Overall assessment

20. Overall, when the available toolkit is applied to banks it addresses the above questions well. For example, the complementary tools provide rough rules-of-thumb on when to worry about build-up of risks in the financial sector. The toolkit identifies the institutions—the weak links—that are vulnerable to adverse shocks in the system; and market-based indicators serve as good near-term indicators of crisis and spillover risks between them. However, many of the above tools apply primarily to bank balance sheets and interlinkages while, as demonstrated by the current crisis, a range of financial institutions (including recently developed institutions such as Central Counterparties) may also be systemically relevant, requiring a broadened focus of the toolkit and methodologies. Persistent data gaps also hinder analytical efforts to assess nonbank financial institutions. Overall, the combination of tools covers the impact of shocks better than their likelihood. While significant progress has been achieved, more work is needed to provide firmer guidance for policymakers on risk buildup and on the design and calibration of adverse stress testing scenarios.

B. Asset Prices: Are Asset Prices Growing Too Fast?

21. Asset Price Models estimate the deviation of an asset market value from its long-term model-based equilibrium, which constitutes a measure of potential for an asset price correction (the assumption being that the larger the misalignment of market prices from fundamental values, the higher the probability of a price correction). The Real estate market model, for instance, provides both (i) direct signals that can be presented in the form of a heat map based on degrees of overvaluation, or (ii) inputs into a model such as the T-model that derives crisis signals based on a benchmark country distribution.

22. More generally, asset price growth features prominently as an early warning signal in Crisis Prediction Models. Sustained equity price inflation or house price acceleration may reflect financial imbalances building up over time and, when combined with a sharp increase in credit-to-GDP gap and banking sector leverage, may flag a looming domestic banking crisis (Credit to GDP-Based Crisis Prediction Model).

23. However, early warning signals from asset price models are not good predictors of the timing of asset price corrections. Parameters in these models are also less reliable during periods of financial stress, because such parameters are derived (implicitly or explicitly) from fundamental-based equilibrium values based on arbitrage-free asset price models. When such assumptions on free arbitrage do not hold (as in periods of financial stress), the estimated equilibrium values become less reliable.

24. In addition, asset price models may also help monitor the initial economic impact of a potential market correction. VAR models, for example, can be used to estimate the response of a set of macroeconomic variables (e.g., real GDP, consumption, investment, or inflation) to house price shocks, taking into account household leverage and risk-sharing provisions in mortgage contracts (i.e., a real estate vulnerability index).

25. Fully-fledged DSGE models are needed to quantify the systemic impact of asset price corrections by incorporating nonlinear effects and feedback loops. Indeed, the macroeconomic impact of asset price booms and busts depends crucially on the behavior of the investor base, the dynamics of household leverage, and the likelihood of a credit crunch, as well as feedback effects on the whole financial sector, which can be aided by the construction of structural DSGE models.

Overall assessment

26. Overall, the available toolkit provides a good set of measures for the size and impact of a potential asset price correction, while its likelihood remains difficult to assess accurately, especially over the near term. It helps construct a variety of scenarios featuring alternative path-dependent asset price dynamics that support the use of other models, including stress test models (see section A). Yet, it could be better linked to investors’ portfolio rebalancing decisions in order to evaluate systemic effects through asset price externalities.

C. Sovereign Risk: How Much is Sovereign Risk a Source of Systemic Risk?

27. The build-up of sovereign risk can be assessed through Debt Sustainability Analysis (DSA), which typically projects public debt/GDP dynamics over 5 years under baseline and adverse scenarios (e.g., decline in growth rate, sharp rise in interest rate, and sustained increase in primary deficits). Such an analysis offers a first assessment of sovereign risk buildup, but stress scenarios used in DSA are more akin to sensitivity analysis (their plausibility is not measured). In addition, Indicators of Fiscal Stress (IFS) provide a summary measure of the risk of a fiscal crisis over the medium term, based on a coincident indicator of rollover pressures and on a forward-looking index of fiscal stress.

28. DSA and IFS can be combined with forecasting tools such as Crisis Prediction Models that aim to measure the likelihood of a fiscal crisis (over a one year horizon), by combining asset prices, measures of external and fiscal imbalances, and data on the financial, household, and corporate sectors. In addition, Schaechter and others (2012) construct a range of indicators to monitor fiscal vulnerability and identify the main underlying fiscal challenges. The choice of indicators is guided by their ability to capture immediate funding pressures, medium and long term funding needs, and risks to the baseline debt dynamics. They can be used to monitor fiscal vulnerabilities in a large set of advanced economies.

29. In turn, a number of tools can be used to analyze the effect of sovereign risk on financial distress. Macro Stress Tests may investigate the impact of a decline in government bond prices on financial institutions both directly, through their liquidity and market risk exposures, and indirectly, through a decline in GDP growth (e.g., caused by fiscal consolidation) and increased credit risk. Complementing this approach, Distress Dependence Model can also use high-frequency market data to measure the probability of distress of a financial institution or financial system conditional on sovereign distress. The sovereign Funding Shock Scenarios (FSS) can be used along with DSA to do forward-looking analysis to assess sovereign’s vulnerability to sudden investor (funding) outflows and banks’ potential exposure to sovereign debt.

30. In addition to the above, some tools can help monitor the potential for negative feedback between financial sector risks and sovereign risk. For example, there may be concerns that the government balance sheet may not be strong enough to meet contingent liabilities reflecting the existence of (explicit or implicit) public guarantees, leading to increased systemic risk. The Systemic CCA allows gauging the impact of such negative feedback effects between sovereign risk and systemic risk.

Overall assessment

31. Overall, the available tools allow for in-depth assessments of the linkages between sovereign risk and systemic risk, as they cover most risk dimensions, financial institutions, time horizons, and country categories, as well as the impact of shocks and their likelihood. However, they do not provide clear signals as to whether sovereign risk buildup has reached a critical level that threatens financial stability, or whether it may unleash perverse dynamics leading to a systemic financial crisis and a sovereign debt crisis.

D. Broader Economy: What are the Amplification Channels among Sectors and through the Domestic Economy?

32. The interconnections and risk exposures among the financial, public, and other sectors can play a key role in magnifying systemic risk. For instance, they may give rise to concentration risks as well as compounded maturity, currency, and capital structure mismatches. The set of Encouraged FSIs provides snapshots of household and corporate leverage and enables comparisons across countries. More detailed analysis of balance sheet data in key sectors (public, private financial, private nonfinancial, household and nonresident) through the Balance Sheet Approach (BSA) facilitates cross-sectoral assessments of maturity, currency, and capital structure mismatches. The BSA tool can be used to stress test sectoral positions by assuming shocks related to interest rates and exchange rates. It also provides an indication of the likelihood that an adverse shock may get amplified into a systemic crisis.

33. Credit growth episodes may also be associated with asset (e.g., real estate) price bubbles, posing a greater threat to financial stability. As such, Asset Price models that provide indicators of such bubbles may usefully complement the above tool (section B). More generally, combinations of credit growth, leverage, and asset price growth, such as in the Credit to GDP-Based Crisis Prediction Model, can be used to estimate relatively well the risk of systemic banking crises about two to three years in advance (section F).

34. A number of tools help assess more deeply the risks arising from linkages across sectors, including indirectly through second round effects. For instance, Asset Price Models can also help measure the vulnerability of the household and corporate sectors to asset price corrections, as well as the broader spillover effects on GDP (section B), although they do not take into account feedback loops through the impact of lower growth on asset price levels. As noted above, Debt Sustainability Analysis (DSA) also examines the impact of real economy, market, and financial system shocks on sovereign risk (Section C), and can be combined with the Systemic Contingent Claims Approach (SCCA) to obtain complementary and more forward-looking estimates of these impacts (sections A and C). Macro Stress Tests assess the impact of a wide range of risks and adverse scenarios on financial institutions, individually or in aggregate. Importantly, however, feedback effects on the economy, including through credit supply conditions, are not appropriately covered in stress test models at this point.

35. Beyond sector-specific linkages, some tools combine cross-sectoral interdependences to assess spillovers of systemic, economy-wide relevance. In particular, the GDP at Risk model forecasts systemic real and financial sector tail risks using time series indicators of financial and real activity. This complex model may not be overly user-friendly, but it captures the dynamic responses of systemic risk indicators to structural shocks, and may provide useful early warnings of systemic events. Moreover, DSGE models provide an in-depth understanding of the interactions and shock transmission across sectors and with the broader economy, including by capturing inter-sectoral and macroeconomic dynamics (e.g., cyclical fluctuations). However, these models are particularly difficult to calibrate and interpret.

Overall assessment

36. Overall, the available toolkit addresses several key inter-sectoral linkages and related risk buildup. However, further efforts are needed to combine these approaches into integrated, economy-wide measures of systemic risk. In particular, there is a need to incorporate feedback and second-round effects across sectors in order to fully capture sectoral risk transfers and enhance the spillover analysis. One example is the gap in stress tests on links between financial sector stress and credit supply conditions, the impact of these conditions on the real economy, and feedback effects on financial sector stress.

E. Cross-Border Linkages: What are the Amplification Channels through Cross-Border Spillovers?

37. Encouraged FSIs related to geographical distribution of loans and foreign-currency denominated liabilities are a starting point for the analysis of cross-border exposures as they may indicate that, on aggregate, a financial system is exposed to credit risk from certain countries or is vulnerable to funding risk from cross-border sources.

38. A more forward-looking perspective on the buildup of cross-border spillover risks is provided by balance of payments and international investment position data, such as data on capital inflows and outflows, and on changes in banks’ foreign liabilities. These can be combined in the T-model to obtain threshold-based signals of a potential financial crisis.

39. Macro Stress Tests also increasingly take into account cross-border linkages in identifying adverse scenarios (as relevant in each country case). Indeed, in order to assess domestic financial institutions’ solvency and liquidity positions comprehensively, they need to capture a range of risks (e.g., foreign credit, liquidity, foreign sovereign and foreign market risks) arising from cross-border exposures and related risks and scenarios in other jurisdictions.

40. In order to assess more deeply and dynamically the interdependences that may generate cross-border spillovers among financial systems or institutions, policymakers should ideally have access to the necessary data on actual interlinkages between such financial institutions and systems. In this case, network models can be used to gauge such spillovers due to shocks in any one, or more, financial institutions (e.g., the G-SIFIs) or among financial systems. Specifically, BIS data can be used to run the two network models: the Cross-Border Network model can be used to calculate different types of connections (first-round impact) between financial systems and estimate the probability of a domestic financial crisis, while the Cross-Border Banking Contagion model can be used to run a network analysis (including multiple-round spillovers) of solvency and funding risk from each financial system to the country. These models provide information on potential spillovers through direct exposures, but they do not offer information on how the system might behave during crises, when both direct and indirect (including common) exposures come into play.

41. In the absence of full cross-exposure data, or in order to complement the above analyses, spillover models based on market data—such as JDI, Returns Spillovers (or Diebold-Yilmaz, DY), Distress Spillovers (DS), Systemic CCA (SCCA)—can be used to assess potential reactions and spillovers between financial institutions across borders, either under normal (DY) or extreme conditions (JDI, DS, SCCA).

Overall assessment

42. The available tools tend to capture somewhat better the impact of cross-border shocks than their likelihood. However, data limitations with regard to cross-border exposures, especially among individual institutions (e.g., G-SIFIs) and with other sectors in foreign countries, remain a serious obstacle to in-depth analyses of cross-border contagion risks.

F. Crisis Risks: What is the Probability of a Systemic Crisis?

43. Several tools extract information from asset prices to estimate the probability of a crisis occurring within a certain time interval.4 Specifically, the Systemic CCA and JDI can be directly applied to estimate the probability that a certain number of institutions will jointly fail in the near-term, thereby triggering financial instability. The systemic CCA can also indicate the probability that the aggregate losses of the financial system will be above a certain specified amount. Alternatively, the Regime Switching Model estimates the probability that financial markets will enter into a state of high volatility or “crisis.” Finally, the SLRI model can be used to assess the probability of systemic liquidity pressures in capital markets.

44. However, while the above tools (relying primarily on asset price data) generally signal crisis events with a relatively high degree of confidence, they offer only limited lead time (e.g., a month or, at most, a year). This may not be sufficient from a policymaker’s perspective. In addition, they are subject to increased error risks when markets incorrectly price risks, for example in the case of illiquid markets.

45. In order to obtain measures of crisis probability with longer lead time, policymakers should also rely on techniques that combine information on aggregate credit growth with other macroeconomic or balance sheet indicators. In particular, the Crisis Prediction Model yields direct measures of the probability of a financial crisis associated with excessive credit growth or private sector leverage (among other variables). And the T-model can signal the increased likelihood of crisis materialization, without providing numeric estimates for the probability of such events. However, these techniques are subject to the typical limitations associated with reduced-form econometric models, and may in particular under-estimate crisis probabilities (relative to the actual occurrence of systemic events).

46. DSGE models combine a broad range of variables, including output, consumption or asset prices, and provide an in-depth understanding of macro-financial linkages and how these could behave under stressed conditions, or in reaction to particular policy actions. In addition, the specification and estimation of these models may not depend on high-frequency information contained in asset prices, allowing them to overcome some of the problems with the other techniques discussed above. However, they rely on numerous assumptions about the structure of the economy, increasing the likelihood of misspecification errors.

Overall Assessment

47. While combining available tools to estimate the likelihood of a crisis can be valuable to policymakers, these tools taken individually are subject to important limitations. The ability of asset-price-based models to accurately estimate crisis probability declines precipitously with time. Structural models overcome these limitations, but at the cost of misspecification errors, which are also pervasive in reduced-form statistical techniques. Therefore, DSGE and Crisis Prediction models can be applied to cross-check whether contemporaneous increases in crisis probability emanating from financial market data are corroborated by longer term measures of risk build-up. Conversely, authorities should use models based on high-frequency asset price data to monitor the intensification of pressures if structural or econometric models have indicated, in the past, the increased probability of stress.

IV. Sample Country Case Study

48. This section aims to provide a concrete illustration of the use of the systemic risk monitoring toolkit in a fictitious country case (Figure 4). The diagrammatic presentation of the key questions from the previous section provides a practical guide to policy makers in the form of a systemic risk monitoring dashboard that can be tailored to each country’s specific circumstances and key risk factors at a given point in time. The illustration uses an unidentified advanced country as an example.

Figure 4.
Figure 4.
Figure 4.

Systemic Risk Dashboard for a Fictitious Country X at end-2007

Summary Assessment: Overall, the set of tools suggests that Country X is about to face intensified financial stress, although the extent of the crisis and its implications for economic growth are unclear. From the dashboard presented below, a policymaker could formulate a first assessment of key sources of systemic risks. In this example, Country X is facing financial stresses that could have a systemic impact in the financial sector. Sovereign risk is heightened by contingent liabilities. Contagion risks from financial sector problems in partner countries would have a large domestic impact. Among asset market indicators, house prices are clearly decelerating, while consumer credit growth has slowed along with a sharp drop in banking stability indicators. However, amplification channels through the broader financial system and domestic economy, while uncertain, do not yet seem to play a significant role. Also, there is no strong signal that a full-fledged financial crisis is about to materialize, even though its probability is rising.

Citation: IMF Working Papers 2013, 168; 10.5089/9781484383438.001.A001

49. The systemic risk dashboard combines (complementary) tools and allows to construct a comprehensive “story” about a country’s key systemic risk at a point in time. The sample dashboard for country X, at end-2007, addresses the six questions successively in six chart panels, and provides a summary of the key observations under each panel as follows:

  • Panel A: Credit growth has slowed down and banking stability is falling fast and below 2003 levels at end-2007. Systemic risk is starting to materialize.

    This panel combines a low-frequency indicator of credit growth (change in the credit-to-GDP ratio) with a high frequency, market-based indicator of systemic risk in the banking system (Distance to Default). Together, this combination provides insights on the particular phase of systemic risk among financial institutions. Consumer credit growth has fallen below 2001 levels. The market-price based measure shows that banking sector vulnerabilities are heightened.

  • Panel B: There are mixed signals from asset prices: house prices are falling (red) for Country X and for countries to which Country X’s banks are exposed. However, not all equity market models are showing misalignments for Country X and its trading partners. This panel combines heat-maps of house prices and equity prices to detect signs of overheating in asset markets. The indicators are calculated for many countries, putting Country X’s situation in a cross-country perspective. Together with Panel A, it seems that financial sector difficulties could be increasing as of end-2007.

  • Panel C: There are clear signals that fiscal risks are increasing, especially from financial sector-related contingent liabilities.

    This panel assesses sovereign-bank linkages through public contingent liabilities (Debt Sustainability Analysis) and potential changes in banks’ holdings of sovereign debt under stress scenarios. Debt Sustainability Analysis shows that the debt/GDP could rise substantially should contingent liabilities materialize. Such liabilities could be related to the financial sector. The Sovereign Funding Shock Scenarios (FSS) show that under certain scenarios, bank holdings of public debt may increase sharply, leading to stronger sovereign-bank linkages in the country.

  • Panel D: There is limited evidence that financial sector shocks are spilling over into the real sector at this stage, although spillover risk within financial institutions is slowly rising.

    This panel focuses on risk amplification across sectors and the economy (GDP-at-Risk and Financial Stability-at-Risk), and adverse feedback loops between contingent public liabilities and banking sector distress (Systemic Contingent Claims Analysis). The Financial Stability at Risk (FSaR) and GDP at Risk (GDPaR) are the worst possible realization, at 5 percent probability, of quarterly growth in real GDP and in the equity returns of a large portfolio of financial firms, respectively. At end-2007, it is unclear from the GDPaR that intensified financial sector stress could spillover to GDP growth. However, the Systemic Contingent Claims Analysis confirms that sovereign contingent liabilities are increasing.

  • Panel E: Country X continues to be strongly connected to the rest of the world, both in terms of actual cross-border balance sheet linkages of banks and potential spillover risks from market contagion.

    This panel illustrates cross-border spillover and contagion risks from two complementary perspectives: Joint Distress Indicators based on market-prices, and Network Analysis using BIS data. Network Analysis of bilateral cross-border banking claims shows that the vulnerability of X from countries A and B are very high. Market-based Joint Distress Indicators are showing a rise in spillover risks between X and four other countries.

  • Panel F: The estimated likelihood of a systemic crisis has increased, but is still small. This panel directly estimates the likelihood of a systemic crisis, either a banking sector crisis or a broader economic crisis, based on complementary probability models. The credit-based banking crisis model shows an uptick in crisis probability, and so does a more general crisis prediction model.

V. Key Findings and Operational Implications

50. On balance, several dimensions of systemic risk are covered well by the toolkit. Tools exist to address most of the key sources of shocks and transmission channels, and appear to do so relatively well along the following dimensions:

  • Impact of shocks rather than the likelihood of systemic events.

  • Long-term buildup of balance sheet vulnerabilities.

  • Spillovers across financial entities.

  • Cross border contagion between banking systems.

51. A number of operational implications emerge from the above discussion:

  • Tools should be combined to exploit their complementarities. Such complementarities help to cross check and confirm the materiality of sources of systemic risk stemming from domestic macro-financial imbalances (e.g. credit boom, asset price bubble, unsustainable public debt) and cross-border linkages, or individual institution exposures (e.g. size, leverage, interconnectedness). Therefore, they help practitioners to avoid overreacting to a single signal, or being lulled into a false sense of security.

  • The selection of tools should be country-specific. Not all tools are applicable or relevant in all country circumstances (e.g., due to specific data requirements).

  • The use of various tools should reflect the typical phases of systemic risk:

    • The slow buildup of risk (e.g., through combinations of balance-sheet and slow-moving indicators).

    • The identification of weak points and potential adverse shocks (e.g., stress tests to detect weak financial institutions, asset price deviation from fundamentals).

    • The fast unfolding of crises, including through amplification mechanisms (e.g., high frequency market-based spillover measures).

  • Longstanding data gaps remain an obstacle to assessing key systemic risk components, including interlinkages and common exposures, which is increasingly problematic in light of the growing complexity of financial crises.

52. However, from the perspective of guiding macroprudential policy, the systemic risk monitoring toolkit is incomplete. The systemic risk monitoring framework is work in progress in a number of key dimensions. Tools exist to assess most sectors and levels of aggregation, but they provide only partial coverage of potential risks and only tentative signals on the likelihood and impact of systemic risk events. As such, they may not provide sufficient comfort to policymakers. Indeed, a number of practical and theoretical roadblocks remain that currently limit our capacity to measure systemic risk in comprehensive and accurate ways:

  • Early warning. The forward-looking properties of systemic risk measures are generally weak, even though some measures appear relatively promising, such as combinations of credit-to-GDP and asset valuation measures, and certain high-frequency market-based indicators.

  • Thresholds. Policymakers need clear and reliable signals indicating when to “worry” and when to take action, and allowing them to monitor the impact of such action over time. Despite recent progress, further work is needed in this area.

  • System’s behavior. The capacity to model aggregate agent behaviors is limited in several areas, such as banks’ approaches to internalizing the materialization or increasing likelihood of systemic risk, potential reverse feedbacks and multi-round effects (i.e., “perfect storms”), and nonlinear risk correlations during periods of financial distress.

53. More broadly, the incomplete nature of the toolkit highlights the need to avoid mechanistic, or narrow, approaches to systemic risk monitoring. The successful use of quantitative diagnostic tools depends critically on the use of sound judgment. Policymakers should not be led to believe that some quantitative approaches (e.g., stress tests or crisis prediction models) are “all-in-one” tools for systemic risk assessments. Indeed, such assessments should bring together not only various types of tools, but also qualitative information, based on market intelligence or on a thorough analysis of a country’s macroeconomic and financial stability frameworks.

Table 1.

Characteristics of Different Systemic Risk Monitoring Tools—A Summary

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Note: “Y” implies that the indicator can be used for the categories; a blank implies the indicator cannot, as yet, be used for the categories unless otherwise noted.Under Publications, P=Published in peer reviewed journal/book; W= Working Paper; F=IMF policy and other multilateral surveillance papers; O=Other publications available online.

Appendix. Tools Binder

Tools for Systemic Risk Monitoring

February 2013

I. Conditional Value-at-Risk (COVAR)

The CoVaR uses market data to assess the contribution of an individual financial institution to systemic risk. It is easy to use/update and has good in-sample forecasting properties for systemic stress, but does not identify the underlying spillover channels.

Tool Snapshot

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Methodology

Quantile regressions are used to derive time-varying CoVaR. Specifically, the measure of contribution of an institution to systemic risk is ΔCoVaR: the difference between the VaR of the financial system conditional on the distress of a particular financial institution i and the VaR of the financial system conditional on the median state of the institution i.

Quantile regressions—the 5th and the 50th-of the weekly returns (growth in market value of assets), of institution I, Xit, and the system, Xsystemt are estimated, conditional on state variables, Mt-1. The Libor-OIS spread and the weekly change in the yield curve (defined as the spread between the 10-year Treasury bond yield and the 3-month Treasury bill yield) are used in M.

Xti=αi+γiMt1+εti,Xtsystem=αsystem|i+βsystem|iXti+γsystem|iMt1+εtsystem|i

The predicted/fitted values are used to derive the following at q=5% and q=50%:

VaRti(q)X^ti=α^qi+γ^qiMt1,CoVaRti(q)X^tsystem=α^system|i+β^system|iVaRti(q)+γ^system|iMt1.

Finally, the ΔCoVaR of each institution is simply:

ΔCoVaRti(5%)=CoVaRti(5%)CoVaRti(50%)=β^system||i(VaRti)(5%)VaRti(50%)
Example

Country/Financial Institutions: 17 U.S. financial institutions covering commercial and investment banks. The chart shows the time-varying ΔCoVaR of the financial system and the systemic contribution of Bear Stearns to overall stress. A value of 7.2 represents the loss-rate in the system when a portfolio of firms moves from their median state to a distress-state.

uA01fig01

United States: Delta-CoVaR

(17 financial institutions)

Citation: IMF Working Papers 2013, 168; 10.5089/9781484383438.001.A001

Source: Arsov and others (2013).
II. Joint Distress Indicators

The set of Joint Distress Indicators (JDI) includes a time-varying measure of joint probability of distress (JPoD) between financial institutions or sovereigns, with nonlinear distress dependence. These indicators can be used to construct a Financial Institutions Stability Index (FISI) reflecting the expected number of financial institutions (FIs) becoming distressed given that at least one FI has become distressed. It can also be used to assess banks’ inter-linkages by computing pair-wise conditional probabilities of distress. The JDI provides complementary perspectives of systemic risk and FIs’ exposure and contribution to systemic risk.

Tool Snapshot

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Methodology

A distress dependence measure is based on estimating the Consistent Information Multivariate Density Optimizing (CIMDO)-density of the banking system that captures time-varying linear and nonlinear distress dependence among banks. Denote by p(x, y, r) the CIMDO-density of the financial system defined by FIs X, Y, and R.

The Joint Probability of Distress (JPoD) is estimated by integrating the density function over the tail of the distribution. It is used as an input to construct all banking stability measures.

JPoD=p(x,y,r)dxdydr

The FISI reflects the expected number of FIs becoming distressed given that at least one FI has become distressed. Denote by xdx,xdy,xdr the distress threshold of return for FIs x, y, and r, respectively. The FISI is defined as: FISI=P(Xxdx)+P(Yxdy)+P(Rxdr)1P(X<xdx,Y<xdy,R<xdr)

Bank interlinkages are assessed by estimating the following conditional probabilities. First, the probability of distress of bank X conditional on bank Y being distressed is computed. This measure captures X’s exposure to bank Y’s distress: P(Xxdx|Yxdy)=P(Xxdx,Yxdy)P(Yxdy)

Second, the PCE is the probability that at least one FI becomes distressed given that X has become distressed. This measure reflects X’s systemic importance in the banking system:

PCEX=P(Y|X)+P(R|X)P(YR|X)
Example

This analysis has been applied to estimate the stability of a set of six Swedish banks using daily CDS spreads over January 2007–October 2010. Figure 1 graphs the evolution of FISI over time. Table 1 shows the PCE conditional on column i FI defaulting computed on a pre-crisis date and at the event of collapse of Lehman Brothers. Table 2 shows the distress dependence matrix, i.e., the conditional probability of row i’s FI defaulting given column j’s default, also computed on September 15, 2008.

Figure 1.
Figure 1.

Sweden: Financial Institutions Stability Index (FISI)

Citation: IMF Working Papers 2013, 168; 10.5089/9781484383438.001.A001

Source: IMF Staff estimates.
Table 1.

Probability of Cascade Effects (PCE) from default of an FI

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Table 2—

Distress Dependence Matrix (DiDe)

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III. Returns Spillovers

The spillover measure suggested by Diebold and Yilmaz (2009), DY, is a time-varying indicator of outward returns-spillovers of institutions—the contribution of one institution to systemic risk. The indicator uses market data on returns (CDS spreads or equity prices) to estimate average (not ‘extreme’) contributions and is easy to use/update. It also has good in-sample forecasting properties for systemic stress, but does not identify the underlying spillover channels, except those between institutions.

Tool Snapshot

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Methodology

Vector Auto regressions (VAR) of the weekly returns of all institutions are used to derive DY. Specifically, the variance decomposition (VD) at a particular lag (say, 10th) is used to derive a matrix of the portion of variance of the shocks to one institution attributable to another institution. Variance decompositions allow us to assess the fraction of the 10-step-ahead error variance in forecasting xi that is due to shocks to xj, ∀ j≠i, for each i. The DY measure of spillover contributions of institution i is the percentage of institution i in the total VD of all institutions. The measure is based on central moments, rather than extreme (tail-risk) movements.

Example
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Source: Based on Arsov and others (2013).

The table shows the variance decomposition based on a VAR(2 lags) of weekly equity returns (in excess of S&P500 returns) of the top 17 United States financial institutions based on the crisis sample 2007–2011, in percent. Banks in columns represent the ‘triggers’ of shocks, and those in rows, the ‘recipient’ of shocks. The third row from the bottom shows the contribution of bank i in columns to spillovers into others and is the sum of all the rows under ‘i’. The last row (spillover index) computes the same thing, but as percentage of all potential spillovers into others (967.3). For instance, bank 1 is the largest contributor of spillovers, with 25 percent of all spillovers into others, 11 percent by bank 7. From this matrix, bank 1 has the most contribution, and bank 7 has the second-most contribution, to systemic stress. Overall spillover index for the period is 57 (or 0.57 expressed as a fraction).

This matrix could be repeated for windows of data to get a rolling sample, in which case a time-series of the DY index can be derived. A more generalized spillover definition is provided in Diebold and Yilmaz (2012).

IV. Distress Spillovers

This is an indicator of outward-spillovers of institutions or markets during extreme times—the potential contribution of one institution to systemic risk during crisis. The indicator uses market data on returns (based on either CDS spreads or equity prices) to estimate extreme contributions and is easy to use/update. It had reasonable predictions for the interconnectedness among 25 largest banking groups in the world (with pre-crisis data) that proved to be true during the 2007–2009 crisis. It does not identify the exact spillover channels, only those between institutions.

Tool Snapshot

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Methodology

It first identifies all extreme events in the data—usually comprising weekly or daily returns on equities, CDS spreads, or market value of assets—by looking at the 1st or the 5th percentile of the joint distribution of returns. All returns lying in the left-tail, that is, the ones below the thresholds, are called ‘exceedances’. Then distress-dependence is estimated by using a logit model to account for the fatness of the tails of the distribution of exceedances. In particular, the probability of an exceedance is estimated conditional on exceedances in other financial institutions or centers, after controlling for common shocks such as extreme conditions in the world equity markets, the country’s stock markets and real sector indicators. The distress-dependence matrices are largely static—the sample periods are fairly long. The analyses could also be extended to make it more time-varying by repeating the exercise over a rolling window, albeit one that is sufficiently long to provide an adequate number of observations of extreme movements.

Example
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Source: IMF Staff estimates.

The table shows the distress dependence between 17 US financial institutions—with 1 indicating the presence (or not) of contagion (to others) potential before the 2007-2009 crisis, at the 5 percent level of significance. The matrix is filled in from logit regressions of the probability of one institution being in distress, conditional on another institution being in distress, controlling for overall market indicators. The rows are the trigger institutions followed by a constant, change in the VIX and MSCI World index. “SC” denotes spillover coefficient. For example, if institution 4 is the trigger, then it contributes to 7.4 percent of all possible outward spillovers. Overall, total spillover coefficient is 81/(16*17) = 0.30. This can be compared to another period. The table can also be replicated for the marginal effects derived from the regressions, in which case the intensity of spillovers can be derived.

V. Market-Based Probability of Default

A market-based default measure provides a forward-looking indicator of default risk by estimating the likelihood that an institution’s future value of assets will fall below its distress point. It combines market data on traded equity (market cap, equity return, and equity volatility) or traded CDS, with balance sheet data on outstanding debt to construct default measures at different horizons.

Tool Snapshot

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Methodology

This methodology applies the insight by Black, Scholes, and Merton that views a firm’s debt as an option on the asset value of the firm. An option valuation approach can thus be applied to assess the default risk of a firm with traded equity (or credit spreads). The distance to default (dtd) at time t of a firm with inferred value of assets Vt, asset volatility σt2, face debt value D, risk-free rate of return r, and time to maturity (T-t) is given by:

dtdtT=ln(VtD)+(rσt22)(Tt)σt(Tt)

Under standard distributional assumptions in the stochastic process of the firm’s value, the risk-neutral PD is characterized by:

PDtT=1N(dtdtT)

Moody’s KMV has applied this valuation framework to compute a physical measure of default risk. Using a long time series with over 30,000 public companies worldwide, it has identified the proportion of firms with a certain distance to default that actually defaulted within a specific forecasting window. This is the expected default frequency (EDF). For nontraded firms with active CDS markets (including sovereigns) KMV offers estimates of the PD, LGD, and risk premium embedded in credit spreads and derives a CDS-implied EDF credit measure.

Example

The Moody’s KMV methodology has been applied to estimate the individual EDF of the largest five banks in Spain and Italy. The figure below shows the asset value-weighted EDF for the Spanish and Italian banking sector. The waves in bank credit distress unleashed in February 2009 and April 2011 comove positively with the spikes in government credit risk reflected in the sovereign CDS market.

uA01fig02

EDF Banking Sector and Sovereign CDS

Citation: IMF Working Papers 2013, 168; 10.5089/9781484383438.001.A001

Source: Authors’ calculation.
VI. Debt Sustainability Analysis (DSA)

DSA examines the effect on the public debt-to-GDP dynamics of several shocks such as real interest rate shock, GDP shock, and a realization of contingent liabilities including financial sector bailout, specified as an exogenous increase in the debt ratio of 10 percent of GDP. It is easy to use and update, but is not linked to any estimate of shocks.

Tool Snapshot

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Methodology

The sensitivity test on sovereign risk by DSA consists of three steps.

The first step sets a baseline scenario on key economic variables such as GDP growth rate and inflation rate as well as interest rate on public debt. The second step projects public debt to GDP ratio using estimated flows of revenue and expenditure under the baseline scenario. The final step examines the dynamics of public debt to GDP ratio under several shock scenarios including rise in real interest rate, decline in GDP growth rate and a realization of contingent liabilities by 10 percent of GDP. The specification of shock scenario is not based on any estimates. So, for example, the contingent liability test should be refined by several approaches such as stress test that provides an estimate of the fiscal cost of bank recapitalization in case of the materialization of various risks, cross country evidence on past banking system crises that presents crude estimates of the possible contingent liabilities, and other estimates of contingent liabilities by sophisticated method such as systemic CCA.

Example

The chart shows the dynamics of public debt-to-GDP ratio if one time 10 percent of GDP shock to contingent liabilities occurs in 2010. DSA related documents of individual countries are available on the DSA website (http://www.imf.org/external/pubs/ft/dsa/ind ex.htm).

uA01fig03

Real depreciation and contingent liabilities shocks

Citation: IMF Working Papers 2013, 168; 10.5089/9781484383438.001.A001

VII. Indicators of Fiscal Stress

This methodology provides a framework to assess fiscal vulnerability and evaluate the likelihood of a full-blown fiscal crisis based on the construction of a coincident indicator of rollover pressure and a forward-looking index of extreme fiscal stress. It can be used as an effective fiscal monitoring tool of sovereign risk based on fiscal fundamentals.

Tool Snapshot

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