## 1. Characteristics of Different Systemic Risk Monitoring tools—A Summary

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### Introduction and purpose
- Objective: clarify the nature and use of currently available systemic risk monitoring tools; assess tools‘ ability to capture dimensions of systemic risk; offer suggestions on how to use tools given their nature, focus, merits, and limitations.
- Scope exclusions: does not analyze the direct relevance of specific systemic risk measures for the selection or calibration of macroprudential policy tools.
- Four complementary ways to use the guide:
  - In-depth discussion of six key policymaker questions (including excessive risk in financial institutions; asset price dynamics; sovereign risk; amplification channels domestically and cross-border; probability of systemic crisis).
  - A living inventory (“Tools Binder”) with two-page snapshots of each tool.
  - A sample systemic risk Dashboard for a fictitious advanced country (illustrates combinations of complementary tools).
  - Tool selection tables summarizing which tools are available for which purpose and country category.

### Definition, crisis phases, and measurement challenges
- Definition:
  - Systemic risk: 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.
  - Macroprudential policy objective: limit system-wide financial risk by enabling policymakers to know better when to “sound the alarm” and implement policy responses.
- Crisis phases (analytical distinction):
  - Buildup phase:
    - Risk builds over time from exposures to overheating sectors, increased risk-taking, financial innovation, or growing cross-border exposures and funding sources.
    - Monitoring focus: assessing likelihood of a systemic crisis and balance between potential financial losses and existing buffers.
  - Shock materialization:
    - Crisis imminent; mounting imbalances make the system fragile to exogenous shocks (GDP, fiscal, exchange rate, housing price shocks; failure of systemically important institution).
    - Monitoring focus: assessing potential losses in financial system and real sector.
  - Amplification and propagation:
    - Shocks propagate via interconnections, potential fire sales, cross-border exposures, adverse feedback loops.
    - Monitoring focus: amplification mechanisms.
- Measurement challenges:
  - More complex shock transmission due to greater integration among institutions, markets, countries, and real sectors.
  - Nonlinear impacts, unstable correlation structures, unpredictable behavioral relationships.
  - Difficulty integrating individual tools into a comprehensive, internally-consistent quantitative framework across sectors, risks, and horizons.

### Key features of the toolkit (coverage and levels)
- Levels of aggregation:
  - Individual financial institutions and markets (market valuation tools; indicators of risk-taking; stress tests).
  - Risk transmission channels (models for time-varying and nonlinear distress dependences and marginal contributions to systemic risk).
  - Whole financial system and economy (crisis prediction and stress test models; general equilibrium models integrating financial and macro variables).
- Types of risk to monitor:
  - Credit risk: assessed via stress testing of default probabilities and Loss Given Default (LGD) relative to macro factors.
  - Liquidity risk: assesses changes to institutions‘ liquidity ratios and market/funding interactions (e.g., via collateralization).
  - Market risk: stress testing for interest rate, exchange rate, asset price shocks; aggregate volatility measures.
- Underlying methodologies:
  - Single risk/soundness indicators:
    - Financial Soundness Indicators (FSIs): balance sheet-based, widely available, backward-looking, often omit PDs and correlation structures.
    - Bank Health Assessment Tool (HEAT): CAMELS-type ratios to derive individual bank indices and aggregate banking soundness.
  - Fundamentals-based models:
    - Macro stress testing, network models: require long-term data, assume parameter stability under stress, produce low-frequency risk estimates.
  - Market-based models:
    - Use high-frequency market data; more dynamic but predictive reliability under stress not firmly established.
  - Hybrid/structural models:
    - Integrate balance sheet data and market prices (e.g., Contingent Claims Analysis (CCA), Distance-to-Default (DtD)).
- Market-based probability-of-default measures:
  - DtD and Expected Default Frequency (EDF): higher-frequency assessment of probability that individual institutions may undergo distress where market prices available.
- Macro Stress Tests:
  - Examine credit, liquidity, and market vulnerabilities under “extreme but plausible” adverse scenarios.
  - Combine risk factors to evaluate capital and liquidity buffer adequacy at aggregate and individual levels.
  - Key challenges: calibration of appropriate and internally consistent shock sets and incorporation of feedback effects to the macroeconomy.

### Toolkit limitations and practical guidance
- No single tool suffices; reliability depends on circumstances—use a range of tools to cover multiple sources of risk.
- Market prices' informational limits: can be undermined during stress and “exuberant” times; may not capture rising interconnectedness.
- Integration gaps: comprehensive frameworks across sectors, risks, and horizons remain in their infancy.
- Practical monitoring approach:
  - Funnel-view sequence: start from narrow financial-sector risks, then assess other sources of systemic risk or amplification (other sectors, broader economy, other countries), and finally measure risk and probability of systemic events.
  - Combine balance sheet indicators (FSIs, HEAT) with market-based PD measures and macro stress tests for fuller assessment of buildup and resilience.

### Financial interdependences and spillover tools
- Network models:
  - Use data on actual interlinkages to gauge spillovers and identify systemically important institutions ("weak links").
  - Applied to aggregate cross-country exposures (BIS data) to gauge cross-border spillover risks.
  - Limitation: provide potential spillovers through direct exposures but not system behavior during crises when indirect/common exposures matter.
- Market-data based spillover models (high-frequency, short-term horizon typically < a year):
  - Examples: Joint Distress Indicators (JDI)/Financial Institutions Stability Index (FISI); Volatility Spillovers (Diebold-Yilmaz (DY)); CoVaR; Distress Spillovers (DS); Systemic CCA (SCCA); Systemic Liquidity Risk Indicator (SLRI).
  - Capabilities: assess spillovers under normal (DY) or extreme conditions (JDI, CoVaR, DS, SCCA); estimate individual institutions’ contributions to systemic stress.
- Overall bank-focus assessment:
  - Strengths: rough rules-of-thumb on when to worry (e.g., credit-to-GDP thresholds from T-model); identify vulnerable institutions and near-term crisis/spillover indicators via market measures.
  - Gaps: many tools primarily apply to bank balance sheets; nonbank financial institutions and Central Counterparties require broader toolkit focus; persistent data gaps on nonbank institutions; tools cover impact better than likelihood; need firmer guidance on risk buildup and stress-test calibration.

### Asset Prices: measures, uses, and limits
- Asset Price Models:
  - Estimate deviation of market value from long-term model-based equilibrium to measure correction potential (e.g., real estate market model provides heat maps).
  - Inputs into crisis prediction models (e.g., Credit to GDP-Based Crisis Prediction Model) when combined with credit-to-GDP gap and banking sector leverage.
- Role in impact analysis:
  - VAR models estimate responses of real GDP, consumption, investment, inflation to house price shocks, accounting for household leverage and mortgage contract provisions (real estate vulnerability index).
  - DSGE models required to quantify systemic impact with nonlinear effects and feedback loops; dependent on investor behavior, household leverage, credit crunch likelihood.
- Limitations:
  - Early warning signals are poor predictors of timing of asset price corrections.
  - Model parameters less reliable during financial stress when equilibrium assumptions may not hold.
  - Could be better linked to investors’ portfolio rebalancing to evaluate systemic effects via asset price externalities.
- Overall: good measures for size and impact of potential corrections; likelihood estimation remains difficult, especially near term.

### Sovereign Risk: assessment and transmission to financial sector
- Tools to assess sovereign risk build-up:
  - Debt Sustainability Analysis (DSA): projects public debt/GDP dynamics over 5 years under baseline and adverse scenarios (e.g., decline in growth rate, sharp rise in interest rate, sustained increase in primary deficits); stress scenarios akin to sensitivity analysis (plausibility not measured).
  - Indicators of Fiscal Stress (IFS): coincident indicator of rollover pressures and a forward-looking index of fiscal stress.
  - Schaechter and others (2012): range of indicators to monitor fiscal vulnerability.
- Tools analyzing sovereign-to-financial distress transmission:
  - Macro Stress Tests: investigate impact of decline in government bond prices on financial institutions directly (liquidity and market risk exposures) and indirectly (GDP decline from fiscal consolidation; increased credit risk).
  - Distress Dependence Model: uses high-frequency market data to measure probability of financial institution/system distress conditional on sovereign distress.
  - Sovereign Funding Shock Scenarios (FSS): used with DSA for forward-looking analysis of sovereign vulnerability to sudden investor outflows and banks’ potential exposure to sovereign debt.
  - Systemic CCA: gauges negative feedback effects between sovereign risk and systemic risk (e.g., contingent liabilities from public guarantees).
- Example scenario values (percent of banking sector assets) from sample dashboard:
  - 0% net financing: 7.5
  - 0% gross financing: 9.4
  - 30% sale: 12.4
- Overall assessment:
  - Tools permit in-depth assessments across risk dimensions, institutions, time horizons, and country categories, covering impact and likelihood of shocks.
  - They do not provide clear signals that sovereign risk buildup has reached a critical level threatening financial stability or will necessarily trigger simultaneous systemic financial and sovereign debt crises.

### Broader economy: cross-sector amplification channels
- Cross-sectoral and balance-sheet analyses:
  - FSIs: snapshots of household and corporate leverage and cross-country comparisons.
  - Balance Sheet Approach (BSA): detailed balance sheet data across public, private financial, private nonfinancial, household, and nonresident sectors; can stress test sectoral positions under interest rate and exchange rate shocks.
- Interactions with asset prices and credit growth:
  - Asset Price models measure household and corporate vulnerability to price corrections and GDP spillovers but do not capture feedback loops from lower growth to asset prices.
  - Combination of credit growth, leverage, and asset price growth (Credit to GDP-Based Crisis Prediction Model) can estimate systemic banking crisis risk about two to three years in advance.
- Cross-sectoral tools:
  - DSA combined with SCCA for complementary forward-looking impact estimates.
  - Macro Stress Tests assess impacts on financial institutions but currently inadequately cover feedback effects on the economy (including via credit supply).
  - GDP at Risk model forecasts systemic tail risks using time series indicators; captures dynamic responses to structural shocks but is complex.
  - DSGE models capture interactions and transmission across sectors but are difficult to calibrate and interpret.
- Overall: toolkit addresses key inter-sectoral linkages and risk buildup, but needs further integration to incorporate feedback and second-round effects across sectors.

### Cross-border linkages: indicators and models
- Starting indicators:
  - FSIs on geographical distribution of loans and foreign-currency liabilities indicate aggregate exposures and vulnerability to cross-border funding risk.
- Forward-looking data:
  - Balance of payments and international investment position data can be combined in the T-model to obtain threshold-based crisis signals.
- Macro Stress Tests:
  - Increasingly incorporate cross-border linkages (foreign credit, liquidity, foreign sovereign and market risks, foreign scenarios) when assessing domestic institutions’ solvency and liquidity.
- Network approaches using BIS data:
  - Cross-Border Network model: calculate first-round connections and estimate probability of a domestic financial crisis.
  - Cross-Border Banking Contagion model: network analysis including multiple-round spillovers of solvency and funding risk.
  - Limitation: do not capture full system behavior during crises when direct and indirect/common exposures matter.
- Market-data spillover models (complementing or substituting cross-exposure data):
  - JDI, Returns Spillovers (Diebold-Yilmaz, DY), Distress Spillovers (DS), Systemic CCA (SCCA) — assess potential reactions and spillovers across borders under normal (DY) or extreme (JDI, DS, SCCA) conditions.
- Overall assessment:
  - Tools capture impact of cross-border shocks better than their likelihood.
  - Serious data limitations on cross-border exposures, especially at individual institution level (e.g., G-SIFIs) and across sectors, hinder in-depth contagion analysis.

### Crisis risks: probability estimation tools and limits
- Asset-price-extraction tools for crisis probability:
  - Systemic CCA and JDI: estimate probability that a certain number of institutions will jointly fail in the near-term; Systemic CCA can indicate probability that aggregate losses exceed a specified amount.
  - Regime Switching Model: estimates probability that financial markets enter a state of high volatility or "crisis."
  - SLRI: assesses probability of systemic liquidity pressures in capital markets.
- Limitations of asset-price-based tools:
  - Signal crisis events with relatively high confidence but offer limited lead time (e.g., a month or, at most, a year).
  - Increased error risk when markets misprice risks (e.g., illiquid markets).
- Longer lead-time approaches:
  - Combine aggregate credit growth with macroeconomic or balance sheet indicators.
  - Crisis Prediction Model: yields direct measures of probability of a financial crisis associated with excessive credit growth or private sector leverage.
  - T-model: signals increased likelihood of crisis materialization without providing numeric probability estimates.
- Model limitations:
  - Reduced-form econometric models may under-estimate crisis probabilities relative to actual systemic events.
  - Application requires ex-ante definitions of what constitutes a crisis (e.g., number of failing banks; joint losses above thresholds; asset price falls below certain levels).

### DSGE models: strengths, limitations, and policy implications
- Strengths:
  - Combine a broad range of variables (output, consumption, asset prices) to provide in-depth understanding of macro-financial linkages and behavior under stress or policy actions.
  - May not depend on high-frequency asset-price information, overcoming some asset-price-model problems.
- Limitations:
  - Rely on numerous structural assumptions, increasing likelihood of misspecification errors.
  - Computationally demanding and require substantial calibration and expertise.
- Recommended use:
  - Use DSGE and Crisis Prediction models to cross-check contemporaneous increases in crisis probability signaled by market data with longer-term measures of risk buildup.
  - Use high-frequency asset price models to monitor intensification of pressures when structural/econometric models have earlier indicated increased probability of stress.
- Example application (IMF, 2011b):
  - Counterfactual analysis of countercyclical capital buffers (CCBs):
    - If an unhealthy house price boom with high probability of ending in crisis occurs, CCBs can cushion crisis effects on real GDP (reduce depth of GDP decline relative to no macroprudential policy).
    - If healthy productivity gains are mistaken for unhealthy house price booms and macroprudential tightening is applied, policy misidentification can permanently lower the real GDP level.
  - Policy implication: trade-off between mitigating crisis risk and risking undue drag on growth when shocks are misclassified.

### Sample country case study — Systemic Risk Dashboard (Country X, end-2007)
- Panel A — Is excessive risk building up in financial institutions?
  - Credit growth slowed; banking stability falling fast and below 2003 levels at end-2007.
  - Systemic risk starting to materialize.
  - Consumer credit growth below 2001 levels.
  - Market-price based Distance to Default shows heightened banking sector vulnerabilities.
- Panel B — Are asset prices growing too fast?
  - Mixed signals: house prices falling for Country X and countries to which X's banks are exposed.
  - Not all equity market models show misalignments for Country X and trading partners.
  - Heat-maps place Country X in a cross-country perspective.
- Panel C — How much is sovereign risk a source of systemic risk?
  - Fiscal risks increasing, especially from financial sector-related contingent liabilities.
  - DSA shows debt/GDP could rise substantially should contingent liabilities materialize.
  - Sovereign Funding Shock Scenarios indicate under certain scenarios bank holdings of public debt may increase sharply, strengthening sovereign-bank linkages.
  - Example scenario values (percent of banking sector assets):
    - 0% net financing: 7.5
    - 0% gross financing: 9.4
    - 30% sale: 12.4
- Panel D — Are financial sector shocks spilling over into the real sector?
  - Limited evidence of spillovers to the real sector at this stage; financial-institution spillover risk slowly rising.
  - Uses GDP-at-Risk (GDPaR) and Financial Stability-at-Risk (FSaR): worst realization at 5 percent probability of quarterly growth in real GDP and equity returns of a large financial portfolio.
  - At end-2007, GDPaR does not clearly indicate intensified financial stress would spill over to GDP growth.
  - Systemic CCA confirms increasing sovereign contingent liabilities.
- Panel E — Is Country X strongly connected internationally?
  - Country X remains strongly connected via cross-border banking claims and potential market contagion.
  - Network Analysis shows vulnerability of X from countries A and B are very high.
  - Market-based Joint Distress Indicators show rise in spillover risks between X and four other countries.
- Panel F — What is the estimated likelihood of a systemic crisis?
  - Estimated likelihood increased but remains small.
  - Credit-based banking crisis model shows an uptick in crisis probability; a more general crisis prediction model shows a similar uptick.
- Summary assessment for Country X at end-2007:
  - Toolkit suggests Country X is about to face intensified financial stress; extent and implications for economic growth are unclear.
  - Key risks: financial stresses with potential systemic impact in the financial sector; heightened sovereign risk via contingent liabilities; significant contagion risk from partner countries.
  - No strong signal of an imminent full-fledged financial crisis, although probability is rising.

### Key findings and operational implications (overview)
- Strengths of tools:
  - Cover impact of shocks more consistently than likelihood of events.
  - Capture long-term buildup of balance sheet vulnerabilities.
  - Identify spillovers across financial entities and cross-border contagion between banking systems.
- Recommended practices:
  - Combine tools to exploit complementarities and cross-check signals to avoid overreacting to a single indicator.
  - Select tools country-specifically, considering data availability and relevance.
  - Use a phase-reflective mix:
    - Slow buildup: balance-sheet and slow-moving indicators.
    - Identification of weak points and adverse shocks: stress tests, asset price deviations.
    - Fast unfolding/amplification: high-frequency market-based spillover measures.
  - Address longstanding data gaps, especially on interlinkages and common exposures.
- Limitations and research needs:
  - Early warning: forward-looking properties are generally weak; promising measures include combinations of credit-to-GDP and asset valuation measures, and some high-frequency market indicators.
  - Thresholds: need clear, reliable signals indicating when to act and how to monitor impacts.
  - System behavior modeling: limited capacity to model aggregate agent behaviors, banks' internalization of systemic risk, reverse feedbacks, multi-round effects, and nonlinear correlations during distress.
  - Emphasis: avoid mechanistic or narrow approaches; combine quantitative tools with qualitative information, market intelligence, and country-specific analysis.

*Source: _wp13168 - 1. Characteristics of Different Systemic Risk Monitoring tools—A Summary; canonical URL: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13168.pdf*

### 1. Characteristics of Different Systemic Risk Monitoring tools—A Summary ......................27

### 1. Characteristics of Different Systemic Risk Monitoring tools—A Summary

### Introduction and purpose
- 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.
- Objective: clarify the nature and use of currently available systemic risk monitoring tools; assess tools‘ ability to capture dimensions of systemic risk; offer suggestions on how to use tools given their nature, focus, merits, and limitations.
- Scope exclusions: does not analyze the direct relevance of specific systemic risk measures for the selection or calibration of macroprudential policy tools.
- Four complementary ways to access and use the guide:
  - An in-depth discussion of six key questions policymakers are likely to ask (Is potentially excessive risk building up in financial institutions? Are asset prices growing too fast? How much is sovereign risk a source of systemic risk? What are amplification channels among sectors and through the broader domestic economy? What are amplification channels through cross-border spillovers? What is the probability of a systemic crisis?).
  - A living inventory (“Tools Binder”) with two-page snapshots of each tool (methodology, coverage, interpretation, data requirements, example use).
  - A sample systemic risk Dashboard for a fictitious advanced country (illustrates combinations of complementary tools).
  - Tool selection tables summarizing which tools are available for which purpose and country category.

### Definition, phases, and measurement challenges
- Definition: systemic risk is 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. Objective of macroprudential policy is to limit system-wide financial risk by enabling policymakers to know better when to “sound the alarm” and implement policy responses.
- Crisis phases (analytical distinction):
  - Buildup phase:
    - Systemic risk builds up over time due to exposures to overheating sectors, increased risk-taking (e.g., competition for market-share or lax supervision), financial innovation, or growing cross-border exposures and funding sources.
    - Monitoring focus: assessing likelihood of a systemic crisis and balance between potential financial losses and existing buffers (Figure 2).
  - Shock materialization:
    - Crisis about to start; mounting imbalances make the financial system fragile and susceptible to exogenous shocks (e.g., GDP or fiscal shocks, exchange rate or housing price shocks, failure of a systemically important financial institution).
    - Monitoring focus: assessing potential losses in both the financial system and the real sector.
  - Amplification and propagation:
    - Shocks affect financial institutions, markets, other sectors, and potentially other countries; monitoring focus on amplification mechanisms (interconnections, potential fire sales, crossborder exposures, adverse feedback loops) (Figure 3).
- Measurement challenges:
  - More complex shock transmission channels due to greater integration among institutions, markets, countries, and real sectors.
  - Greater scope for nonlinear impacts (e.g., illiquid markets), unstable correlation structures, and unpredictable behavioral relationships.
  - Difficulty integrating individual tools into a comprehensive, internally-consistent quantitative framework across sectors, types of risk, and time horizons.

### Key features of the toolkit (coverage and levels)
- Levels of aggregation for tools:
  - Individual financial institutions and markets (e.g., market valuation tools for housing, equity or bond markets; indicators of risk-taking; stress testing tools).
  - Risk transmission channels (models capturing time-varying and nonlinear distress dependences and marginal contributions of institutions to systemic risk).
  - Whole financial system and economy (crisis prediction and stress test models capturing system-wide impairment and macro-financial linkages; general equilibrium models integrating financial and macroeconomic variables).
- Types of risk to monitor:
  - Credit risk: key source of risk; stress testing assesses probabilities of default and Loss Given Default (LGD) in relation to macro factors.
  - Liquidity risk: tools assess changes to institutions‘ liquidity ratios and interactions between market liquidity and funding conditions (e.g., via collateralization).
  - Market risk: stress testing for interest rate, exchange rate, or asset price shocks; aggregate market volatility measures to assess latent vulnerabilities.
- Underlying methodologies:
  - Single risk/soundness indicators:
    - Balance sheet-based indicators such as Financial Soundness Indicators (FSIs) widely available; cover many risk dimensions but tend to be backward-looking and often do not account for probabilities of default or correlation structures.
    - Bank Health Assessment Tool (HEAT) builds on CAMELS-type ratios to derive individual bank indices and monitor aggregate banking soundness.
  - Fundamentals-based models:
    - Rely on macroeconomic or balance sheet data (e.g., macro stress testing, network models) to assess macro-financial linkages and interconnectedness; often require long-term data series, assume parameter stability under stress, and produce low-frequency risk estimates.
  - Market-based models:
    - Use high-frequency market data to track rapidly-changing conditions; more dynamic but predictive reliability under stress is not firmly established.
  - Hybrid, structural models:
    - Integrate balance sheet data and market prices to estimate shock impacts on defaults or credit growth (examples: Contingent Claims Analysis (CCA), Distance-to-Default (DtD)).
- Market-based probability-of-default measures:
  - Distance-to-Default (DtD) and Expected Default Frequency (EDF) can assess with higher frequency the probability that individual financial institutions may undergo distress or fail, where relevant market prices are available.
- Macro Stress Tests:
  - Examine sources of financial institution vulnerability across credit, liquidity, and market risks under “extreme but plausible” (tail risk) adverse scenarios.
  - Combine risk factors to evaluate capital and liquidity buffer adequacy at aggregate and individual institution levels.
  - Key challenges: calibration of appropriate and internally consistent sets of shocks across risk factors and incorporating feedback effects from financial sector problems into the macroeconomy.

### Toolkit limitations and practical guidance
- No “all-in-one” tool: reliability depends on circumstances; policymakers should use a range of tools to cover multiple potential sources of risk.
- Informational limits of market prices: may be undermined during stress and “exuberant” times and may not capture rising interconnectedness.
- Integration gaps: efforts to build comprehensive frameworks integrating tools across sectors, risks, and horizons are still in their infancy.
- Practical monitoring approach:
  - Adopt a funnel-view sequence: start from narrow sources of risk within the financial sector, then assess other sources of systemic risk or amplification (other sectors, broader economy, other countries), and finally measure risk and probability of systemic events.
  - Combine balance sheet indicators (e.g., FSIs, HEAT) with market-based PD measures and macro stress tests for a fuller assessment of risk buildup and resilience.

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### 17.      From a more aggregate and forward-looking perspective, credit growth is often

### From a more aggregate and forward-looking perspective, credit growth is often central to the buildup of macro-financial risk

### Financial interdependences and spillover tools
- Network models (using data on actual interlinkages) gauge spillovers triggered by shocks in any one, or more, financial institutions; can identify systemically important institutions (the "weak links").
- Network models applied to aggregate cross-country exposures (e.g., based on BIS data) gauge cross-border spillover risks among financial systems.
- Limitations: network models provide information on potential spillovers through direct exposures but do not offer information on how the system might behave during crises, when both direct and indirect (e.g., common) exposures come into play.
- Market-data based models allow high-frequency monitoring of spillover likelihood and systemic stress within a short-term horizon (typically less than a year). Examples:
  - Joint Distress Indicators (JDI)/Financial Institutions Stability Index (FISI)
  - Volatility Spillovers (Diebold-Yilmaz (DY))
  - CoVaR
  - Distress Spillovers (DS)
  - Systemic CCA (SCCA)
  - Systemic Liquidity Risk Indicator (SLRI) — provides a coincident indicator of systemic liquidity shortages during market distress.
- These market-data models:
  - Can assess spillovers under normal (DY) or extreme conditions (JDI, CoVaR, DS, Systemic CCA).
  - Do not trace specific risk channels of spillovers, but can estimate individual institutions’ contributions to systemic stress.

### Overall assessment (bank focus)
- Toolkit strengths:
  - Provides rough rules-of-thumb on when to worry about financial sector risk buildup (e.g., credit-to-GDP thresholds from T-model).
  - Identifies vulnerable institutions ("weak links") and near-term crisis/spillover indicators via market-based measures.
- Gaps and limitations:
  - Many tools apply primarily to bank balance sheets and interlinkages; other potentially systemically relevant institutions (including Central Counterparties) require broadened toolkit focus.
  - Persistent data gaps hinder analysis of nonbank financial institutions.
  - Combination of tools covers impact of shocks better than their likelihood.
  - Need firmer guidance for policymakers on risk buildup and on design and calibration of adverse stress testing scenarios.

### Asset Prices: Are Asset Prices Growing Too Fast?
- Asset Price Models estimate deviation of market value from long-term model-based equilibrium as measure of potential for price correction.
  - Real estate market model: provides (i) direct signals (heat map of overvaluation degrees) and (ii) inputs into models such as the T-model to derive crisis signals based on benchmark country distribution.
- Role in Crisis Prediction Models:
  - Sustained equity price inflation or house price acceleration, 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).
- Limitations:
  - Early warning signals are poor predictors of timing of asset price corrections.
  - Model parameters are less reliable during financial stress because equilibrium assumptions (arbitrage-free) may not hold.
- Use in impact analysis:
  - VAR models estimate responses of macro variables (real GDP, consumption, investment, inflation) to house price shocks, accounting for household leverage and mortgage contract provisions (i.e., a real estate vulnerability index).
  - Fully-fledged DSGE models needed to quantify systemic impact by incorporating nonlinear effects and feedback loops; depend on investor behavior, household leverage dynamics, likelihood of credit crunch, and feedbacks on the financial sector.
- Overall assessment:
  - Toolkit provides good measures for size and impact of potential asset price correction; likelihood remains difficult to assess accurately, especially near term.
  - Could be better linked to investors’ portfolio rebalancing to evaluate systemic effects through asset price externalities.

### Sovereign Risk: How Much is Sovereign Risk a Source of Systemic Risk?
- Tools to assess sovereign risk build-up:
  - Debt Sustainability Analysis (DSA): projects public debt/GDP dynamics over 5 years under baseline and adverse scenarios (e.g., decline in growth rate, sharp rise in interest rate, sustained increase in primary deficits); stress scenarios akin to sensitivity analysis (plausibility not measured).
  - Indicators of Fiscal Stress (IFS): summary measure of fiscal crisis risk over the medium term, based on a coincident indicator of rollover pressures and a forward-looking index of fiscal stress.
  - Schaechter and others (2012): range of indicators to monitor fiscal vulnerability (choice guided by capturing immediate funding pressures, medium and long term funding needs, and risks to baseline debt dynamics).
- Combining tools:
  - DSA and IFS can be combined with Crisis Prediction Models (one year horizon) that use asset prices, external and fiscal imbalances, and data on financial, household, and corporate sectors.
- Tools analyzing sovereign-to-financial distress transmission:
  - Macro Stress Tests: investigate impact of decline in government bond prices on financial institutions directly (liquidity and market risk exposures) and indirectly (decline in GDP growth from fiscal consolidation; increased credit risk).
  - Distress Dependence Model: uses high-frequency market data to measure probability of financial institution/system distress conditional on sovereign distress.
  - Sovereign Funding Shock Scenarios (FSS): used with DSA for forward-looking analysis of sovereign vulnerability to sudden investor outflows and banks’ potential exposure to sovereign debt.
  - Systemic CCA: gauges negative feedback effects between sovereign risk and systemic risk (e.g., contingent liabilities from public guarantees).
- Overall assessment:
  - Tools allow in-depth assessments across risk dimensions, institutions, time horizons, and country categories, covering impact of shocks and their likelihood.
  - They do not provide clear signals on whether sovereign risk buildup has reached a critical level threatening financial stability or will unleash perverse dynamics leading to simultaneous systemic financial crisis and sovereign debt crisis.

### Broader Economy: Amplification channels among sectors and through the domestic economy
- Cross-sectoral and balance-sheet analyses:
  - Encouraged FSIs: snapshots of household and corporate leverage and cross-country comparisons.
  - Balance Sheet Approach (BSA): detailed balance sheet data in public, private financial, private nonfinancial, household, and nonresident sectors; facilitates assessment of maturity, currency, and capital structure mismatches; can stress test sectoral positions assuming interest rate and exchange rate shocks; indicates likelihood that adverse shock may amplify into systemic crisis.
- Interactions with asset prices and credit growth:
  - Asset Price models measure household and corporate vulnerability to asset price corrections and broader GDP spillovers but do not capture feedback loops from lower growth to asset prices.
  - Combinations of credit growth, leverage, and asset price growth (Credit to GDP-Based Crisis Prediction Model) can estimate risk of systemic banking crises about two to three years in advance.
- Cross-sectoral tools:
  - Debt Sustainability Analysis (DSA) combined with Systemic Contingent Claims Approach (SCCA) for complementary forward-looking impact estimates.
  - Macro Stress Tests assess impacts of a wide range of risks on financial institutions, individually or in aggregate, but currently do not appropriately cover feedback effects on the economy (including through credit supply conditions).
  - GDP at Risk model forecasts systemic real and financial sector tail risks using time series indicators of financial and real activity; captures dynamic responses to structural shocks but is complex and not user-friendly.
  - DSGE models capture interactions and shock transmission across sectors and the broader economy, but are difficult to calibrate and interpret.
- Overall assessment:
  - Toolkit addresses key inter-sectoral linkages and risk buildup, but further efforts needed to combine approaches into integrated, economy-wide measures of systemic risk.
  - Need to incorporate feedback and second-round effects across sectors to fully capture sectoral risk transfers (example gap: stress tests on links between financial sector stress and credit supply, impact on real economy, and feedback on financial sector stress).

### Cross-Border Linkages: Amplification channels through cross-border spillovers
- Starting indicators:
  - Encouraged FSIs on geographical distribution of loans and foreign-currency denominated liabilities indicate aggregate exposures to specific countries and vulnerability to cross-border funding risk.
- Forward-looking data:
  - Balance of payments and international investment position data (capital inflows/outflows, changes in banks’ foreign liabilities) can be combined in the T-model to obtain threshold-based crisis signals.
- Macro Stress Tests:
  - Increasingly incorporate cross-border linkages to capture foreign credit, liquidity, foreign sovereign, and foreign market risks and scenarios in other jurisdictions when assessing domestic institutions’ solvency and liquidity.
- Network approaches using BIS data:
  - Cross-Border Network model: calculate first-round connections between financial systems and estimate probability of a domestic financial crisis.
  - Cross-Border Banking Contagion model: run network analysis including multiple-round spillovers of solvency and funding risk from each financial system to the country.
  - Limitation: provide potential spillovers through direct exposures but not system behavior during crises when direct and indirect (including common) exposures matter.
- Market-data spillover models (when cross-exposure data absent or to complement networks):
  - JDI, Returns Spillovers (Diebold-Yilmaz, DY), Distress Spillovers (DS), Systemic CCA (SCCA) — assess potential reactions and spillovers across borders under normal (DY) or extreme conditions (JDI, DS, SCCA).
- Overall assessment:
  - Available tools capture impact of cross-border shocks better than their likelihood.
  - Data limitations on cross-border exposures, especially among individual institutions (e.g., G-SIFIs) and with other sectors in foreign countries, remain serious obstacles to in-depth contagion analysis.

### Crisis Risks: What is the Probability of a Systemic Crisis?
- Asset-price-extraction tools for crisis probability:
  - Systemic CCA and JDI: estimate probability that a certain number of institutions will jointly fail in the near-term, triggering financial instability; Systemic CCA can indicate probability that aggregate losses exceed a specified amount.
  - Regime Switching Model: estimates probability that financial markets enter a state of high volatility or "crisis."
  - SLRI: assesses probability of systemic liquidity pressures in capital markets.
- Limitations of asset-price-based tools:
  - Signal crisis events with relatively high confidence but offer limited lead time (e.g., a month or, at most, a year).
  - Increased error risk when markets misprice risks (e.g., illiquid markets).
- Longer lead-time approaches:
  - Combine information on aggregate credit growth with macroeconomic or balance sheet indicators.
  - Crisis Prediction Model: yields direct measures of probability of a financial crisis associated with excessive credit growth or private sector leverage (among other variables).
  - T-model: signals increased likelihood of crisis materialization without providing numeric probability estimates.
- Model limitations:
  - Reduced-form econometric models may under-estimate crisis probabilities relative to actual systemic events.
  - The application of these tools requires ex-ante definitions of what constitutes a crisis (e.g., number of failing banks; joint losses above thresholds; asset price falls below certain levels).

*IMF staff paper content unit excerpt*

### 46.      DSGE models combine a broad range of variables, including output,

### 46.      DSGE models combine a broad range of variables, including output,

### DSGE models: strengths and limitations
- 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.
- They may not depend on high-frequency information contained in asset prices, allowing them to overcome some problems of high-frequency asset-price-based techniques.
- They rely on numerous assumptions about the structure of the economy, increasing the likelihood of misspecification errors.

### Overall assessment of tools for crisis likelihood estimation
- Combining available tools to estimate crisis likelihood can be valuable, but tools taken individually have important limitations.
- Asset-price-based models: ability to accurately estimate crisis probability declines precipitously with time.
- Structural models: overcome some limitations of asset-price models but introduce misspecification risk.
- Recommendation on use:
  - Use DSGE and Crisis Prediction models to cross-check whether contemporaneous increases in crisis probability from financial market data are corroborated by longer term measures of risk build-up.
  - Use high-frequency asset price models to monitor intensification of pressures when structural/econometric models have earlier indicated increased probability of stress.

### Sample country case study — Systemic Risk Dashboard (Country X, at end-2007)
- Purpose: provide a practical systemic risk monitoring dashboard tailored to country circumstances; example uses an unidentified advanced country ("Country X") at end-2007.
- The dashboard addresses six successive questions in six chart panels (Panels A–F), summarizing key observations:

  - Panel A: Is excessive risk building up in financial institutions?
    - Summary findings:
      - Credit growth has slowed down and banking stability is falling fast and below 2003 levels at end-2007.
      - Systemic risk is starting to materialize.
      - Consumer credit growth has fallen below 2001 levels.
      - Market-price based Distance to Default shows banking sector vulnerabilities are heightened.

  - Panel B: Are asset prices growing too fast?
    - Summary findings:
      - Mixed signals from asset prices: house prices are falling (red) for Country X and for countries to which Country X's banks are exposed.
      - Not all equity market models show misalignments for Country X and its trading partners.
      - Heat-maps of house prices and equity prices place Country X in a cross-country perspective.

  - Panel C: How much is sovereign risk a source of systemic risk?
    - Summary findings:
      - Clear signals that fiscal risks are increasing, especially from financial sector-related contingent liabilities.
      - Debt Sustainability Analysis shows debt/GDP could rise substantially should contingent liabilities materialize.
      - Sovereign Funding Shock Scenarios (FSS) show that under certain scenarios, bank holdings of public debt may increase sharply, strengthening sovereign-bank linkages.
      - Example scenario values (percent of banking sector assets):
        - 0% net financing: 7.5
        - 0% gross financing: 9.4
        - 30% sale: 12.4

  - Panel D: Are financial sector shocks spilling over into the real sector?
    - Summary findings:
      - 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.
      - Uses GDP-at-Risk (GDPaR) and Financial Stability-at-Risk (FSaR): defined as 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, GDPaR does not clearly indicate intensified financial stress would spill over to GDP growth.
      - Systemic Contingent Claims Analysis confirms sovereign contingent liabilities are increasing.

  - Panel E: Is Country X strongly connected internationally?
    - Summary findings:
      - Country X remains strongly connected to the rest of the world via cross-border banking claims and potential market contagion.
      - Network Analysis of bilateral cross-border banking claims shows vulnerability of X from countries A and B are very high.
      - Market-based Joint Distress Indicators show a rise in spillover risks between X and four other countries.

  - Panel F: What is the estimated likelihood of a systemic crisis?
    - Summary findings:
      - Estimated likelihood of a systemic crisis has increased, but remains small.
      - Credit-based banking crisis model shows an uptick in crisis probability; a more general crisis prediction model shows a similar uptick.

- Summary assessment for Country X at end-2007:
  - The toolkit suggests Country X is about to face intensified financial stress, though extent and implications for economic growth are unclear.
  - Key risks: financial stresses with potential systemic impact in the financial sector; heightened sovereign risk via contingent liabilities; significant contagion risk from partner countries.
  - Asset market indicators: house prices clearly decelerating; consumer credit growth slowed; banking stability indicators dropped sharply.
  - Amplification channels through the broader financial system and domestic economy appear uncertain and not yet dominant.
  - No strong signal of an imminent full-fledged financial crisis, although probability is rising.

### Key findings and operational implications (overview)
- Tools cover several dimensions of systemic risk reasonably well:
  - Impact of shocks rather than likelihood of systemic events.
  - Long-term buildup of balance sheet vulnerabilities.
  - Spillovers across financial entities.
  - Cross-border contagion between banking systems.

- Operational implications and recommended practices:
  - Combine tools to exploit complementarities and cross-check signals to avoid overreacting to a single indicator.
  - Select tools country-specifically, considering data availability and relevance.
  - Use tools reflecting typical phases of systemic risk:
    - Slow buildup of risk (balance-sheet and slow-moving indicators).
    - Identification of weak points and potential adverse shocks (stress tests, asset price deviations).
    - Fast unfolding of crises and amplification mechanisms (high-frequency market-based spillover measures).
  - Address longstanding data gaps, especially on interlinkages and common exposures.

- Limitations and areas needing further work:
  - Early warning: forward-looking properties of systemic risk measures are generally weak; some promising measures include combinations of credit-to-GDP and asset valuation measures, and certain high-frequency market-based indicators.
  - Thresholds: policymakers need clear, reliable signals indicating when to act and how to monitor impacts; further work needed.
  - System behavior modeling: limited capacity to model aggregate agent behaviors, banks' internalization of systemic risk, reverse feedbacks and multi-round effects, and nonlinear risk correlations during periods of financial distress.
  - Emphasis: avoid mechanistic or narrow approaches; combine quantitative tools with qualitative information, market intelligence, and thorough country-specific analysis.

*Source: Excerpt from the IMF Working Paper chapter on systemic risk monitoring (sample country case study and toolkit assessment).*

### 23. DSGE Model

### 23. DSGE Model

### Tool Snapshot
- Systemic reach: Banks, nonfinancial corporations, households.
- Forward-looking properties: Counterfactual analysis.
- Ease of use: Difficult to use (requires experience).
- Identification of linkages: Provides in-depth understanding of interactions and shock transmission across sectors.
- Likelihood (PD) or impact (LGD)?: LGD.
- Coverage: Banks, nonfinancial corporations, households.
- Types of risk covered: Procyclicality stemming from shocks related to real estate prices, lax lending standards, productivity.
- Main output: The macro-financial impact of various shocks with and without macroprudential policies.
- Other outputs: Leading indicators of future financial instability.
- Thresholds: Not available.
- Time horizon: Flexible.
- Data requirements: Various, depending upon calibration requirements.
- Reference: Benes and others, 2010; Users: IMF, 2011b.

### Methodology
- Model structure:
  - Embeds a banking sector within a new-Keynesian model of the real sector.
  - Key features: strong role of balance sheets of banks and nonfinancial borrowers in shock propagation; link between banks’ diversifiable (idiosyncratic) lending risk and aggregate nondiversifiable macroeconomic risk from cycles.
  - Flexible parameters to mimic different economies (extent of foreign-currency lending; central bank exchange rate management; sensitivities of imports and exports to exchange rate; ease of banks raising fresh equity).
- Analytical capabilities:
  - Traces movements of numerous macroeconomic and financial variables in response to alternative shock sources.
  - Can analyze macro-financial effects of macroprudential instruments (e.g., countercyclical capital buffers and loan-to-value ratio caps).
  - Focuses on procyclicality; does not cover interconnectedness.
- Practical considerations:
  - Requires considerable experience to run and calibrate.
  - Calibration-dependent results; demands sectoral balance-sheet and macroeconomic calibration inputs.

### Example and Key Findings
- Application described in IMF (2011b):
  - Use case: assess effects of countercyclical capital buffers (CCBs) under two shock types:
    - Healthy productivity gains (do not lead to crisis).
    - Unhealthy house price boom followed by a crisis.
  - Findings:
    - If an unhealthy house price boom with high probability of ending in crisis occurs, countercyclical capital buffers can successfully cushion crisis effects on real GDP levels (CCBs reduce the depth of GDP decline relative to no macroprudential policy).
    - If healthy productivity gains are mistaken for unhealthy house price booms and macroprudential tightening is applied, macroprudential policy can permanently lower the real GDP level (policy misidentification can cause long-term harm).
- Policy implication:
  - DSGE-based policy evaluation highlights the trade-off between mitigating crisis risk and risking undue drag on growth when shocks are misclassified.
  - Effective use requires careful identification of the nature of shocks (healthy vs. unhealthy), robust calibration, and consideration of possible permanent costs from inappropriate macroprudential tightening.

### Use Cases and Limitations
- Use cases:
  - Counterfactual policy analysis for macroprudential instruments.
  - Exploration of shock transmission across banks, corporates, and households.
  - Generation of leading indicators derived from model-implied dynamics.
- Limitations:
  - Covers procyclicality but not interconnectedness.
  - Computational and expertise demands are high.
  - Results sensitive to calibration assumptions and model specification.

*Source: IMF, 2011b.*

### References

### References

### Systemic risk measurement and analytics
- Adrian, Tobias and Markus Brunnermeier, 2010, ―CoVaR,‖ Federal Reserve Bank of New York Staff Reports.
- Arsov, Ivailo, Elie Canetti, Laura Kodres and Srobona Mitra, 2013, ―Near-Coincident Indicators of Systemic Stress,‖ IMF Working Paper 13/115, (Washington: International Monetary Fund).
- Bisias, Dimitrios, Mark Flood, Andrew W. Lo, and Stavros Valavanis, 2012, ―A Survey of Systemic Risk Analytics,‖ U.S. Department of the Treasury, Office of Financial Research, Working Paper 0001, available at http://www.treasury.gov/initiatives/wsr/ofr/Pages/ofr-working-papers.aspx
- Borio, C and M Drehmann, 2009, ―Assessing the Risk of Banking Crises—Revisited,‖ BIS Quarterly Review, March.
- Chan-Lau, Jorge, Srobona Mitra, and Li Lian Ong, 2012, ―Identifying Contagion Risk in the International Banking System: An Extreme Value Theory Approach,‖ International Journal of Finance and Economics, available at http://onlinelibrary.wiley.com/doi/10.1002/ijfe.1459/abstract.
- De Nicolò and Lucchetta, 2010, ―Systemic Risks and the Macroeconomy,‖ IMF Working Paper 10/29, and forthcoming NBER book Quantifying Systemic Risk, Joseph G. Haubrich and Andrew W. Lo, edited conference proceedings, University of Chicago Press.
- Diebold, Francis X. and Kamil Yilmaz, 2009, ―Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets,‖ Economic Journal, Vol.119, pp. 15–71.
- ______, 2012, ―Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers,‖ International Journal of Forecasting, Vol. 28, Issue 1, January --- March.
- Espinosa-Vega, M., and J. Sole, 2010, ―Cross-Border Financial Surveillance: A Network Perspective,‖ IMF Working Paper 10/105.
- Gray, Dale, F. and Andreas A. Jobst, 2011, ―Modeling Systemic Financial Sector and Sovereign Risk,‖ Sveriges Riksbank Economic Review, 2011:2.
- Kealhofer, S., 2003 ―Quantifying Credit Risk I: Default Prediction,‖ Financial Analysts Journal, Jan/Feb, pp. 30–44.
- Lopez-Espinosa, G., Moreno, A., Rubia, A., and Valderrama, L., 2012, ―Short-term Wholesale Funding and Systemic Risk: A Global CoVaR Approach,‖ IMF Working Paper 12/46 (Washington: International Monetary Fund).
- Lund-Jensen, K, 2012, ―Monitoring Systemic Risk based on Dynamic Thresholds,‖ IMF Working Paper 12/149 (Washington: International Monetary Fund).
- Moretti, Marina, Stéphanie Stolz, and Mark Swinburne, 2008, ―Stress Testing at the IMF,‖ IMF Working Paper 08/206, (Washington: International Monetary Fund).
- Reint Gropp & Marco Lo Duca & Jukka Vesala, 2009. ―Cross-Border Bank Contagion in Europe,‖ International Journal of Central Banking, International Journal of Central Banking, vol. 5(1), pages 97-139, March
- Segoviano, Miguel and Charles Goodhart, 2009, ―Banking Stability Measures,‖ IMF Working Paper 09/04 (Washington: International Monetary Fund).
- Severo, Tiago, 2012, ―Measuring Systemic Liquidity Risk and the Cost of Liquidity Insurance,‖ IMF Working Paper 12/194 (Washington: International Monetary Fund)
- Sun, Tao, 2011, ―Identifying Vulnerabilities in Systemically-Important Financial Institutions in a Macro-financial Linkages Framework,‖ IMF Working Paper 11/111 http://www.imf.org/external/pubs/cat/longres.aspx?sk=24841.

### Macrofinancial frameworks, fiscal vulnerabilities, and sovereign debt
- Allen, M., Rosenberg, C., Keller, C., Setser, B., and Roubini, N. (2002), ―A Balance Sheet Approach to Financial Crises,‖ IMF Working Paper 02/210 (Washington: International Monetary Fund).
- Arslanalp, Serkan and Takahiro Tsuda, 2012, ―Tracking Global Demand for Advanced Economy Sovereign Debt,‖ IMF Working Paper 12/284, (Washington: International Monetary Fund).
- Baldacci, E., McHugh, J. and Petrova, I., 2011, ―Measuring Fiscal Vulnerabilities and Fiscal Stress: A Proposed Set of Indicators,‖ IMF Working Paper 11/94, (Washington: International Monetary Fund).
- González-Hermosillo, Brenda and Heiko Hesse, 2009, ―Global Market Conditions and Systemic Risk,‖ IMF Working Paper 90/230.
- Laeven and Valencia, 2010, ―Resolution of Banking Crises: The Good, the Bad, and the Ugly,‖ IMF Working Paper 10/146 (Washington: International Monetary Fund).
- Reinhart, C. M. and K. S. Rogoff, 2010, ̳ ̳From Financial Crash to Debt Crisis,‘‘ NBER Working Paper No. 15795 (forthcoming A merican Economic Review).
- Schaechter, A, C. Emre Alper, Elif Arbatli, Carlos Caceres, Giovanni Callegari, Marc Gerard, Jiri Jonas, Tidiane Kinda, Anna Shabunina, and Anke Weber, 2012, ―A Toolkit to Assessing Fiscal Vulnerabilities,‖ IMF Working Paper 12/11.

### Macroprudential policy, liquidity, and banking supervision
- Benes, Jaromir, Michael Kumhof, and David Vavra, 2010, ―Monetary Policy and Financial Stability in Emerging-Market Economies: An Operational Framework,‖ paper presented at the 2010 Central Bank Macroeconomic Modeling Workshop, Manila. www.bsp.gov.ph/events/2010/cbmmw/papers.htm.
- Dell‘ Ariccia, Giovanni, Deniz Igan, Luc Laeven, Hui Tong, Bas B. Bakker, Jérôme Vandenbussche, 2012, ―Policies for Macrofinancial Stability: How to Deal with the Credit Booms,‖ IMF Staff Discussion Note 12/06 http://www.imf.org/external/pubs/cat/longres.aspx?sk=25935
- Basel Committee of Banking Supervision (BCBS), 2012, ―Models and Tools for Macroprudential Analysis,‖ May, available at https://www.bis.org/publ/bcbs_wp21.htm
- International Monetary Fund, 2002, ―Information Note on Modifications to the Fund‘s Debt Sustainability Assessment Framework for Market Access Countries,‖ July.
- _____ , 2003, ―Sustainability Assessments-Review of Application and Methodological Refinements,‖ June.
- ______, 2006, FSI Compilation Guide (http://www.imf.org/external/pubs/ft/fsi/guide/2006/)
- ______, 2007, ―Republic of Croatia: Selected Issues,‖ IMF Country Report No. 07/82.
- ______, 2009a, ―Detecting Systemic Risk,‖ Chapter 3, Global Financial Stability Report, April, http://www.imf.org/External/Pubs/FT/GFSR/2009/01/index.htm
- ______, 2009b, ―Assessing the Systemic Implications of Financial Linkages,‖ Chapter 2, Global Financial Stability Report, April, http://www.imf.org/External/Pubs/FT/GFSR/2009/01/index.htm
- ______, 2010, ―Recovery, Risk, and Rebalancing,‖ Chapter 1, World Economic Outlook, World Economic and Financial Surveys (Washington, October).
- ______, 2011a, ―Japan: Spillover Report for the 2011 Article IV Consultation and Selected Issues,‖ IMF Country Report No. 11/183, July.
- ______, 2011b, ―Towards Operationalizing Macroprudential Policy: When to Act?‖ Chapter 3, Global Financial Stability Report, September.
- ______, 2011c, ―Mexico: 2011 Article IV Consultation,‖ IMF Country Report No. 11/250, July.
- ______, 2011d, ―How to Address the Systemic Part of Liquidity Risk,‖ Chapter 2, Global Financial Stability Report, April.
- ______-Financial Stability Board, 2010, ―The IMF-FSB Early Warning Exercise: Design and Methodological Toolkit,‖ IMF Occasional Paper, available at http://www.imf.org/external/np/exr/facts/ewe.htm.
- Sun, Tao, 2011, ―Identifying Vulnerabilities in Systemically-Important Financial Institutions in a Macro-financial Linkages Framework,‖ IMF Working Paper 11/111 http://www.imf.org/external/pubs/cat/longres.aspx?sk=24841.
- Ong, Li Lian, Phakawa Jeasakul and Sarah Kwoh, 2013, ̳ ̳User Guide: HEAT! A Bank Health Assessment Tool,‘‘ IMF Working Paper, forthcoming.

### Statistical methods and volatility models
- Hamilton, James D., and Raul Susmel, 1994, ̳ ̳Autoregressive Conditional Heteroskedasticity and Changes in Regime,‘‘ Journal of Econometrics, Vol. 64 (September-October), pp. 307---33.

### Banking linkages and cross-border banking
- Čihák, M., Muñoz, S., and Scuzzarella, R., 2011, ―The Bright and the Dark Side of Cross-Border Banking Linkages,‖ IMF Working Paper 11/186, (Washington: International Monetary Fund).
- Chan-Lau, Jorge, Srobona Mitra, and Li Lian Ong, 2012, ―Identifying Contagion Risk in the International Banking System: An Extreme Value Theory Approach,‖ International Journal of Finance and Economics, available at http://onlinelibrary.wiley.com/doi/10.1002/ijfe.1459/abstract.
- Reint Gropp & Marco Lo Duca & Jukka Vesala, 2009. ―Cross-Border Bank Contagion in Europe,‖ International Journal of Central Banking, International Journal of Central Banking, vol. 5(1), pages 97-139, March

*Source: _wp13168 - References*

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