## Executive Summary (tnm1708)

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### Introduction and purpose
- Assessing country risk is a core component of surveillance at the IMF and is conducted through a comprehensive architecture covering bilateral and multilateral dimensions.
- The note describes approaches used internally by Fund staff to examine systemic risks across advanced (AE), emerging (EM), and low-income (LIC) economies.
- The note provides a high-level view of theory and methodologies; an on-line companion guide provides more technical implementation details and will be updated as methodologies evolve.
- Model outputs are not published for market sensitivity reasons but inform bilateral surveillance and the IMF’s flagship global surveillance publications: the World Economic Outlook (WEO), Global Financial Stability Report (GFSR), and Fiscal Monitor.

### Conceptual framework and surveillance architecture
- Risk assessment views crises as the interaction between underlying vulnerabilities and trigger events; vulnerabilities are necessary but not sufficient for crises.
- Predicting timing of crises is challenging; models are better suited to estimating likelihoods and forecasting worst-case outcomes rather than precise timing.
- Fund staff adopt a multi-sectoral approach—assessing fiscal, external, domestic non-financial sectors, financial and asset pricing risks (where appropriate), susceptibility to spillovers and contagion, and policy implementation risks (including political instability or gridlock).
- Surveillance architecture components highlighted:
  - WEO: baseline forecasts and broad global developments including thematic risks.
  - GFSR and Fiscal Monitor: sector-specific global vulnerabilities (financial and fiscal).
  - Early Warning Exercise (EWE): selected tail-risks and risk-specific contingency plans.
  - Internal Global Risk Assessment Matrix (RAM): consolidated key global risks with likelihood and expected impact; prime source for global risks discussed in Article IVs.
  - Tail Risk group: junior staff focused on extreme-tail risk thinking.
  - Bilateral surveillance (Article IV staff reports): detailed country vulnerabilities and country RAMs.
  - Internal Vulnerability Exercise: cross-country, bottom-up identification of emerging risks linking multilateral and country-team assessments; methodologies in this note relate most closely to this exercise.

### Evolution and scope of the Fund’s toolkit
- Toolkit covers AE, EM, and LIC country groups and evolves with economic circumstances, improved data, and new models.
- EM emphasis: predicting vulnerability to sudden stops of capital flows.
- LIC emphasis: vulnerability to sudden growth declines.
- AE approach: use a suite of models to identify sectoral vulnerabilities rather than focusing on a single crisis type due to lack of historical crisis experience.
- New and strengthened elements include:
  - Better coverage of non-financial corporate and household balance sheets in EMs, including debt composition.
  - Strengthened analysis of contagion and spillbacks (e.g., network analysis).
  - Revamped assessment of financial risks with forward-looking indicators, such as credit cycles.
- Final risk assessments combine model outputs (first step) with country-specific knowledge and judgment (second step).

### Supplementary indicators and tools
- Scenario analysis: simulates materialization of prominent global risks to frame coordinated risks and add forward-looking context.
- Policy implementation risk: assesses a country’s capacity to address emerging crises, including political gridlock and limited technical capacity.
- Alternative crisis models: used to test robustness of signals from the main toolkit.

---

### Box 1 — Crisis Vulnerability Assessment: A Signal Extraction Model

### Model overview and purpose
- Uses a common signal extraction approach to assess vulnerability to crises across EMs and LICs.
- Purpose: establish thresholds for key indicators and aggregate indicators that exceed thresholds to produce sectoral and overall vulnerability indices.
- Indicator choice tailored to crisis type (e.g., sudden stop models emphasize external indicators; food decline index uses natural events, food production declines, food-dependence and governance indicators).
- Builds on Kaminsky, Lizondo, and Reinhart (1998).

### Threshold choice and aggregation
- Threshold for each indicator chosen to minimize the sum of the percentage of crises missed and the percentage of non-crises falsely flagged as a crisis (false alarms).
- Equivalent to maximizing the difference between the cumulative distribution functions of the crisis and non-crisis samples.
- Rationale: crises are relatively rare; missing a crisis observation is much more costly than issuing a false alarm (example: if crises are 5 percent of the sample, missing one crisis is as costly as issuing 19 false-alarms).
- Countries receive a 1 if their value of the indicator falls on the risky side of the threshold and a 0 otherwise.
- Indicator dummies are typically averaged with weights given by their signal-to-noise ratio, defined as (1-z)/z, where z is the sum of the fractions of false alarms and missed crises.
- Weights are rescaled to sum to one within a sector; sectoral indices are then combined into an overall index using sectoral weights (sectoral weights reflect sectoral signal-to-noise ratios and staff judgment).
- Advantages: thresholds impart robustness to outliers; aggregation accommodates missing data and allows inclusion of additional indicators without limiting country coverage.

### Sudden stops model — sectoral indicator sets (as specified)
- External: Current Account Balance; REER Misalignment; External Debt to Exports; Private Sector External Debt to GDP; Reserve Coverage as % of ARA Metric; Change in Reserve Coverage as % of ARA Metric; Change in external debt to exports.
- Public: Gross Public Debt; Primary Balance Gap; Average Maturity of Debt; Average Effective Interest Rate; Interest Expense; Gross Financing Needs; Short-term Gross Public Debt; Public External Debt; Foreign Currency share of debt; Cyclically adjusted primary balance.
- Financial: Foreign Liability as % of Domestic Credit; Capital Adequacy Ratio; Return on Assets; Loan to Deposit; 3-year cumulative credit to GDP; End of period gap - Equity; End of period gap - Loan to deposit ratio; End of period gap - Credit to the private sector; End of period gap - Property prices; Interest coverage ratio.
- Real: Real GDP Growth; Non financial corporation foreign currency debt; Household total debt; FX share in total NFC and Household debt outstanding; Non-inv grade debt in total stock of debt securities.
- Contagion: Regional EMBI spreads; Change in export demand growth; External banking liabilities x VIX; External banking liabilities x partner GDP growth; Correlation of EMBI spreads x equity price growth; Deviation from trend banking liabilities.

### Sectoral analysis highlights
- External sector: current account balance and REER misalignment signal external sustainability; private sector external debt to exports and reserve buffers (reserves relative to the IMF ARA metric) are informative for sudden stop vulnerability.
- Public sector: solvency and liquidity captured by gross public debt, primary balance gap, gross financing needs; duration and currency risks captured by average effective interest rates, average maturity, interest expense, public external debt and foreign currency debt shares; cyclically adjusted primary balance evaluates macro-fiscal stance.
- Financial sector: bank capital adequacy and return on assets complemented by foreign liability exposures and measures of financial overheating; where data suffice, financial boom-bust indicators include credit to private sector, loan to deposit ratio, equity prices, and real estate prices; estimated financial cycle gaps (deviations from trend) are used for capital account crisis vulnerability thresholds.
- Real sector: indicators capture household and non-financial corporate balance sheet risks and slower growth; additional variables include (1) foreign exchange denominated corporate debt to GDP, (2) total household debt to GDP, (3) share of foreign exchange denominated debt in total corporate and household debt, and (4) share of non-investment grade debt securities in total stock of outstanding debt securities.
- Contagion: captures cross-country correlations and channels such as trading partners’ demand, domestic banking liabilities to foreign financial institutions, global volatility, and regional bond and equity spillovers.

---

### Vulnerability indices for LICs and frontier economies

### Growth Decline Vulnerability Index for LICs (GDVI)
- LICs face higher output growth volatility driven by terms of trade deterioration, reduced foreign aid and remittances, and natural disasters; amplified by weak institutions, political/social instability, undiversified structures, and limited financial sector development.
- GDVI employs the same signal extraction methodology as for EM sudden stops.
- Event identification steps:
  - Identify “large” negative exogenous shock events: a shock is large if the annual percentage change of any of the following falls below the lower 10th percentile of the country-specific distribution: (i) external demand; (ii) terms-of-trade; (iii) FDI; (iv) aid; (v) remittances; (vi) climatic shocks (large natural disasters).
  - Within shock events, sharp growth decline episodes require: (i) the 2-year average level of real output per capita post-shock falls below the pre-shock 3-year average level; and (ii) output per capita growth is negative in the year of the shock.
- GDVI constructed from core variables to form three sector-level indices: Overall Economy and Institutions; External; Fiscal.
- Examples of GDVI variables:
  - Overall Economy and Institutions: Real GDP Growth; Country Policy and Institutional Assessment (CPIA); GINI Coefficient; Real GDP per capita growth; Number of affected from natural disaster.
  - External: Reserve coverage (GIR/Imports G&S); Real growth in exports of goods and services; Exchange market pressure index; Lagged export-weighted trade partner growth; Lagged export-weighted export price growth.
  - Fiscal: Government balance; Public debt (% GDP); Real government revenue; Government tax revenue.

### Frontier economies — Financial Vulnerability Index (FVI)
- For frontier LICs, an FVI captures likelihood of severe banking system stress as financial integration grows.
- FVI variables: (i) capital adequacy ratio; (ii) return on assets; (iii) bank credit over bank deposits; (iv) cumulative 3-year growth of credit-to-GDP; and (v) foreign liabilities measured as cross-border loans and deposits over domestic credit.
- Variables converted to 0/1 based on estimated thresholds and aggregated using the signal extraction approach.

---

### Advanced economies — multi-tool approach and key models
- AEs use multiple stand-alone tools to assess risks across sectors and channels: External, Macrofiscal, Financial, Asset Price, Contagion.
- Key tools and indicators:
  - External: external imbalances, External Balance Assessment (EBA) and EBA-lite, external balance sheet exposures (net foreign ‘other investment’ assets).
  - Macroeconomic imbalances: one-year-ahead inflation forecast from a BVAR relating inflation to world oil price, output growth, policy interest rates, ten-year term spreads; monetary conditions gap (deviation of policy rates from Taylor-type rule); growth tracking via a high-frequency dynamic factor model using more than 100 series per country.
  - Fiscal: rollover and financing risks (gross financing needs and debt to maturity versus benchmarks); market perception via sovereign CDS spreads and relative asset swap (RAS) spreads; medium- and long-term fiscal adjustment needs (required adjustment in cyclically-adjusted primary balance to target gross public debt ratios); medium-term risk to public debt dynamics via stochastic simulations over a five-year horizon; sensitivity of public debt to adverse shocks—analyzed scenarios include a low growth scenario of one percentage point lower growth than the WEO baseline over five years and a high interest rate scenario of a permanent increase of 100 basis points over the baseline.
  - Financial sector: interbank access indicators (Euro area reliance on ECB funding; for other AEs a composite based on the LIBOR-OIS spread and a Markov regime switching model); fundamentals-implied bank CDS (asset-weighted); credit cycle indicators (change and gap in credit-to-GDP compared to thresholds); balance sheet soundness (loan-to-deposit ratio, shares of foreign currency assets and liabilities, leverage ratio, profitability, asset quality).
  - Asset prices: residential real estate assessed on price misalignment, household debt burden, and mortgage market characteristics; equity market fair value via valuation multiples and an arbitrage pricing model; corporate sector risks from market-cap-weighted firm-level indicators of default probability, stock valuation, profitability, leverage, and liquidity.
  - Contagion: cross-border bank contagion tools measuring downstream and upstream exposures and scenario propagation; trade-channel contagion via bilateral trade exposure measures; exposure to emerging markets using BIS data on advanced economy bank claims on EMs.

---

### Supplementary indicators, scenarios, policy implementation risk, and limitations
- Salient global risks simulated using global models (examples mentioned include G20MOD, FSGM, and GFM) to represent economic effects; simulations inform country teams on growth, exchange rates, bond yields, trade flows, and other variables.
- Simulated scenarios have included shocks such as a sharp growth downturn in China, financial volatility in Europe, monetary policy normalization in the US, and rising geopolitical fragmentation.
- Supplementary approaches provide cross-checks and include models for fiscal, financial, growth (AEs and EMs), and food supply (LICs) crises; these also rely on the signal extraction methodology.
- Policy response capacity is recognized as critical; toolkit’s political/institutional assessment component is nascent and uses external measures of government stability, socioeconomic conditions, political unrest, volatility, and governance.
- Methodological notes and limitations:
  - Thresholds and aggregation emphasize robustness to outliers and accommodate missing data.
  - Early warning exercises face challenges in timing crises and capturing all conceivable risks.
  - Staff judgment is a primary element: model outputs inform final country risk assessments by country economists who apply detailed country knowledge.
  - Ongoing refinement: methodology is being made more accessible and will continue to be improved as better models and cross-cutting indicators emerge.

---

### Key empirical facts and parameters (as reported)
- Fifty-six crisis episodes identified by combining quantitative indicators and narrative analysis over a data set of 49 countries during 1993-2015.
- Example analytic tradeoff: if crises are 5 percent of the sample, missing one crisis is as costly as issuing 19 false-alarms.
- GDVI event threshold: lower 10th percentile of country-specific distribution used to define “large” negative exogenous shocks.
- Fiscal stress scenarios for AEs: low growth scenario of one percentage point lower growth than the WEO baseline over five years; high interest rate scenario of a permanent increase of 100 basis points over the baseline.
- Financial indicators using multi-year horizons: 3-year cumulative credit to GDP and cumulative 3-year growth of credit-to-GDP used in multiple indices.

*Source: tnm1708 - Executive Summary*

### Executive Summary

### Executive Summary

### Introduction and Purpose
- Assessing country risk is a core component of surveillance at the IMF and is conducted through a comprehensive architecture covering bilateral and multilateral dimensions.
- The note describes approaches used internally by Fund staff to examine systemic risks across advanced (AE), emerging (EM), and low-income (LIC) economies.
- The note provides a high-level view of theory and methodologies; an on-line companion guide provides more technical implementation details and will be updated as methodologies evolve.
- Model outputs are not published for market sensitivity reasons but inform bilateral surveillance and the IMF’s flagship global surveillance publications: the World Economic Outlook (WEO), Global Financial Stability Report (GFSR), and Fiscal Monitor.

### Conceptual framework and surveillance architecture
- Risk assessment views crises as the interaction between underlying vulnerabilities and trigger events; vulnerabilities are necessary but not sufficient for crises.
- Predicting timing of crises is challenging; models are better suited to estimating likelihoods and forecasting worst-case outcomes rather than precise timing.
- Fund staff adopt a multi-sectoral approach—assessing fiscal, external, domestic non-financial sectors, financial and asset pricing risks (where appropriate), susceptibility to spillovers and contagion, and policy implementation risks (including political instability or gridlock).
- The surveillance architecture components highlighted:
  - WEO: baseline forecasts and broad global developments including thematic risks.
  - GFSR and Fiscal Monitor: sector-specific global vulnerabilities (financial and fiscal).
  - Early Warning Exercise (EWE): selected tail-risks and risk-specific contingency plans.
  - Internal Global Risk Assessment Matrix (RAM): consolidated key global risks with likelihood and expected impact; prime source for global risks discussed in Article IVs.
  - Tail Risk group: junior staff focused on extreme-tail risk thinking.
  - Bilateral surveillance (Article IV staff reports): detailed country vulnerabilities and country RAMs.
  - Internal Vulnerability Exercise: cross-country, bottom-up identification of emerging risks linking multilateral and country-team assessments; methodologies in this note relate most closely to this exercise.

### Evolution and scope of the Fund’s toolkit
- Toolkit covers AE, EM, and LIC country groups and evolves with economic circumstances, improved data, and new models.
- For EMs: emphasis on predicting vulnerability to sudden stops of capital flows.
- For LICs: emphasis on vulnerability to sudden growth declines.
- For AEs: given lack of historical crisis experience, use a suite of models to identify sectoral vulnerabilities rather than focusing on a single crisis type.
- New and strengthened elements include:
  - Better coverage of non-financial corporate and household balance sheets in EMs, including debt composition.
  - Strengthened analysis of contagion and spillbacks (e.g., network analysis).
  - Revamped assessment of financial risks with forward-looking indicators, such as credit cycles.
- Final risk assessments are made by country teams combining model outputs (first step) with country-specific knowledge and judgment (second step).

### Supplementary indicators and tools
- Scenario analysis: simulates materialization of prominent global risks to frame coordinated risks and add forward-looking context.
- Policy implementation risk: assesses a country’s capacity to address emerging crises, including political gridlock and limited technical capacity.
- Alternative crisis models: used to test robustness of signals from the main toolkit.

### Assessing risk in Emerging Markets: Sudden Stops Model (overview)
- EM risk assessments focus on vulnerability to a sudden and significant stop of capital inflows.
- Approach: estimate likelihood that macroeconomic or financial shocks combined with underlying vulnerabilities could trigger a sudden stop.
- Empirical modeling approach:
  - Early-warning indicators breach estimated thresholds identified via a signal extraction model that minimizes false alarms and missed crises (see Box 1 reference).
  - Fifty-six crisis episodes have been identified by combining quantitative indicators and narrative analysis over a data set of 49 countries during 1993-2015.
  - Indicators span five sectors: external, public, financial, real, and contagion; thresholds are set at levels determined by identified capital account crises.
  - The model has evolved over time: extended dataset, refined thresholds and weights, and incorporation of financial cycle and corporate sector balance sheet indicators.
  - Use of a broad set of indicators recognizes that risks can emanate from various sectors and that aggregation across sectors is required to detect vulnerabilities.

### Implementation and use in surveillance
- Model results serve as input—one component—of country-team assessments; they are integrated with desk judgment to avoid purely mechanical conclusions.
- Methodologies are iteratively updated and new approaches are added to the toolkit or substituted for older models as warranted by experience and data improvements.
- Results are used internally to inform bilateral surveillance and multilateral products even though they are not publicly published.

*Source: tnm1708 - Executive Summary*

### Box 1. Crisis Vulnerability Assessment: A Signal Extraction Model

### Box 1. Crisis Vulnerability Assessment: A Signal Extraction Model

### Model overview
- Uses a common signal extraction approach to assess vulnerability to crises across EMs and LICs.
- Model purpose: establish thresholds for key indicators and aggregate indicators that exceed thresholds to produce sectoral and overall vulnerability indices.
- Definition and choice of indicators are tailored to the crisis type (e.g., sudden stop models emphasize external indicators; food decline index uses natural events, food production declines, food-dependence and governance indicators).
- Building on the work of Kaminsky, Lizondo, and Reinhart (1998).

### Threshold choice
- For each indicator, a threshold is defined to flag elevated vulnerability.
- Threshold chosen to minimize the sum of the percentage of crises missed and the percentage of non-crises falsely flagged as a crisis (false alarms).
- Equivalent to maximizing the difference between the cumulative distribution functions of the crisis and non-crisis samples.
- Rationale: crises are relatively rare in the data; missing a crisis observation is much more costly than issuing a false alarm (example in text: if crises are 5 percent of the sample, missing one crisis is as costly as issuing 19 false-alarms).
- Countries receive a 1 if their value of the indicator falls on the risky side of the threshold and a 0 otherwise.

### Aggregation
- Indicator dummies (ones and zeros) are typically averaged with weights given by their signal-to-noise ratio, defined as (1-z)/z, where z is the sum of the fractions of false alarms and missed crises.
- Where literature supports relative importance, judgment can be used to determine aggregation weights.
- Weights are rescaled to sum to one within a sector; sectoral indices are then combined into an overall index using sectoral weights (sectoral weights reflect sectoral signal-to-noise ratios and staff judgment).
- Advantages: thresholds impart robustness to outliers; aggregation accommodates missing data and allows inclusion of additional indicators without limiting country coverage.

### Sudden stops model — sectoral indicator sets (Table 1)
- External: Current Account Balance; REER Misalignment; External Debt to Exports; Private Sector External Debt to GDP; Reserve Coverage as % of ARA Metric; Change in Reserve Coverage as % of ARA Metric; Change in external debt to exports.
- Public: Gross Public Debt; Primary Balance Gap; Average Maturity of Debt; Average Effective Interest Rate; Interest Expense; Gross Financing Needs; Short-term Gross Public Debt; Public External Debt; Foreign Currency share of debt; Cyclically adjusted primary balance.
- Financial: Foreign Liability as % of Domestic Credit; Capital Adequacy Ratio; Return on Assets; Loan to Deposit; 3-year cumulative credit to GDP; End of period gap - Equity; End of period gap - Loan to deposit ratio; End of period gap - Credit to the private sector; End of period gap - Property prices; Interest coverage ratio.
- Real: Real GDP Growth; Non financial corporation foreign currency debt; Household total debt; FX share in total NFC and Household debt outstanding; Non-inv grade debt in total stock of debt securities.
- Contagion: Regional EMBI spreads; Change in export demand growth; External banking liabilities x VIX; External banking liabilities x partner GDP growth; Correlation of EMBI spreads x equity price growth; Deviation from trend banking liabilities.

### Growth Decline Vulnerability Index for LICs (GDVI)
- LICs face higher output growth volatility driven by terms of trade deterioration, reduced foreign aid and remittances, and natural disasters; amplified by weak institutions, political/social instability, undiversified structures, and limited financial sector development.
- The GDVI employs the same signal extraction methodology as for EM sudden stops.
- Event identification steps:
  - Identify “large” negative exogenous shock events: a shock is large if the annual percentage change of any of the following falls below the lower 10th percentile of the country-specific distribution: (i) external demand; (ii) terms-of-trade; (iii) FDI; (iv) aid; (v) remittances; (vi) climatic shocks (large natural disasters).
  - Within shock events, sharp growth decline episodes require: (i) the 2-year average level of real output per capita post-shock falls below the pre-shock 3-year average level; and (ii) output per capita growth is negative in the year of the shock.
- GDVI constructed from a core set of variables to form three sector-level indices (Table 2): Overall Economy and Institutions; External; Fiscal.
- Examples of GDVI variables:
  - Overall Economy and Institutions: Real GDP Growth; Country Policy and Institutional Assessment (CPIA); GINI Coefficient; Real GDP per capita growth; Number of affected from natural disaster.
  - External: Reserve coverage (GIR/Imports G&S); Real growth in exports of goods and services; Exchange market pressure index; Lagged export-weighted trade partner growth; Lagged export-weighted export price growth.
  - Fiscal: Government balance; Public debt (% GDP); Real government revenue; Government tax revenue.

### Frontier economies — Financial Vulnerability Index (FVI)
- For frontier LICs, an FVI captures likelihood of severe banking system stress as financial integration grows.
- FVI variables: (i) capital adequacy ratio; (ii) return on assets; (iii) bank credit over bank deposits; (iv) cumulative 3-year growth of credit-to-GDP; and (v) foreign liabilities measured as cross-border loans and deposits over domestic credit.
- Variables converted to 0/1 based on estimated thresholds and aggregated using the signal extraction approach.

### Advanced economies — a suite of models (no single aggregation)
- Advanced economies use multiple stand-alone tools to assess risks across sectors and channels: External, Macrofiscal, Financial, Asset Price, Contagion.
- Key tools and indicators:
  - External: external imbalances, External Balance Assessment (EBA) and EBA-lite, external balance sheet exposures (net foreign ‘other investment’ assets).
  - Macroeconomic imbalances: one-year-ahead inflation forecast from a BVAR relating inflation to world oil price, output growth, policy interest rates, ten-year term spreads; monetary conditions gap (deviation of policy rates from Taylor-type rule); growth tracking via a high-frequency dynamic factor model using more than 100 series per country.
  - Fiscal: rollover and financing risks (gross financing needs and debt to maturity versus benchmarks); market perception via sovereign CDS spreads and relative asset swap (RAS) spreads; medium- and long-term fiscal adjustment needs (required adjustment in cyclically-adjusted primary balance to target gross public debt ratios); medium-term risk to public debt dynamics via stochastic simulations over a five-year horizon; sensitivity of public debt to adverse shocks—analyzed scenarios include a low growth scenario of one percentage point lower growth than the WEO baseline over five years and a high interest rate scenario of a permanent increase of 100 basis points over the baseline.
  - Financial sector: interbank access indicators (Euro area reliance on ECB funding; for other AEs a composite based on the LIBOR-OIS spread and a Markov regime switching model); fundamentals-implied bank CDS (asset-weighted); credit cycle indicators (change and gap in credit-to-GDP compared to thresholds); balance sheet soundness (loan-to-deposit ratio, shares of foreign currency assets and liabilities, leverage ratio, profitability, asset quality).
  - Asset prices: residential real estate assessed on price misalignment, household debt burden, and mortgage market characteristics; equity market fair value via valuation multiples and an arbitrage pricing model; corporate sector risks from market-cap-weighted firm-level indicators of default probability, stock valuation, profitability, leverage, and liquidity.
  - Contagion: cross-border bank contagion tools measuring downstream and upstream exposures and scenario propagation; trade-channel contagion via bilateral trade exposure measures; exposure to emerging markets using BIS data on advanced economy bank claims on EMs.

### Supplementary indicators, scenarios, and policy implementation risks
- Salient global risks simulated using global models (examples mentioned include G20MOD, FSGM, and GFM) to represent economic effects; simulations inform country teams on growth, exchange rates, bond yields, trade flows, and other variables.
- Simulated scenarios have included shocks such as a sharp growth downturn in China, financial volatility in Europe, monetary policy normalization in the US, and rising geopolitical fragmentation.
- Supplementary approaches provide cross-checks and include models for fiscal, financial, growth (AEs and EMs), and food supply (LICs) crises; these also rely on the signal extraction methodology.
- Policy response capacity is recognized as critical; toolkit’s political/institutional assessment component is nascent and uses external measures of government stability, socioeconomic conditions, political unrest, volatility, and governance.

### Key methodological notes and limitations
- Thresholds and aggregation emphasize robustness to outliers and accommodate missing data.
- The toolkit improves surveillance by bringing risks into focus, but early warning exercises face challenges in timing crises and capturing all conceivable risks.
- Staff judgment is a primary element: model outputs inform final country risk assessments by country economists who apply detailed country knowledge.
- Ongoing refinement: the methodology is being made more accessible and will continue to be improved as better models and cross-cutting indicators emerge.

*Source: Box 1. Crisis Vulnerability Assessment: A Signal Extraction Model (Technical Notes and Manuals 17/08).*

### REFERENCES

### REFERENCES

### IMF Working Papers and Related IMF Publications
- Abiad, Abdul, and Jonathan D. Ostry, 2005, “Primary Surpluses and Sustainable Debt Levels in Emerging Market Countries,” IMF Working Paper No. 05/6 (Washington: International Monetary Fund).
- Alper, C. Emre, Lorenzo Forni, and Marc Gerard, 2012, “Pricing Sovereign Credit Risk: Evidence From Advanced Economies During the Financial Crisis,” IMF Working Paper No. 12/24 (Washington: International Monetary Fund).
- Andrle, Michal, Patrick Balgrave, Pedro Espaillat, Keiko Honjo, Benjamin Hunt, Mika Kortelainen, René Lalonde, Douglas Laxton, Eleonora Mavroeidi, Dirk Muir, Susanna Mursula, and Stephen Snudden, 2015, “The Flexible System of Global Models – FSGM,” IMF Working Paper No. 15/64 (Washington: International Monetary Fund).
- Celasun, Oya, Xavier Debrun, and Jonathan D. Ostry, 2006, “Primary Surplus Behavior and Risks to Fiscal Sustainability in Emerging Market Countries: A ‘Fan-Chart’ Approach,” IMF Working Paper No. 06/67 (Washington: International Monetary Fund).
- Dabla-Norris, Era, and Yasemin Bal Gündüz, 2012, “Exogenous Shocks and Growth Crises in Low-Income Countries: A Vulnerability Index,” IMF Working Paper No. 12/264 (Washington: International Monetary Fund).
- Laeven, Luc, and Fabian Valencia, 2008, “Systemic Banking Crises; A New Database,” IMF Working Paper No. 08/224, (Washington: International Monetary Fund).
- Laeven, Luc, and Fabian Valencia, 2010, “Resolution of Banking Crises: The Good, the Bad, and the Ugly,” IMF Working Paper No. 10/44 (Washington: International Monetary Fund).
- Laeven, Luc, and Fabian Valencia, 2012, “Systemic Banking Crises Database; An Update,” IMF Working Paper No. 12/163, (Washington: International Monetary Fund).
- Matheson, Troy, 2011, “New Indicators for Tracking Growth in Real Time,” IMF Working Paper No. 11/43 (Washington: International Monetary Fund).
- Philips, Steven, Luis Catao, Luca Ricci, Rudolfs Bems, Mitali Das, Julian Di Giovanni, D. Filiz Unsal, Marola Castillo, Jungjin Lee, Jair Rodriguez, and Mauricio Vargas, 2013, “External Balance Assessment (EBA) Methodology,” IMF Working Paper No. 13/272 (Washington: International Monetary Fund).
- Vitek, Francis, 2015, “Macrofinancial Analysis in the World Economy: A Panel Dynamic Stochastic General Equilibrium Approach,” IMF Working Paper No. 15/227 (Washington: International Monetary Fund).

### IMF Policy Papers, Reports, and Internal Documents
- Independent Evaluation Office of the International Monetary Fund, 2014, IMF Response to the Financial and Economic Crisis (Washington).
- International Monetary Fund, 2007, “Assessing Underlying Vulnerabilities and Crisis Risks in Emerging Market Countries—A New Approach,” SM/07/328, September 17 (Washington).
- International Monetary Fund, 2010, “The IMF-FSB Early Warning Exercise: Design and Methodological Toolkit,” IMF Policy Paper (Washington).
- International Monetary Fund, 2011, “Managing Volatility: A Vulnerability Exercise for Low-Income Countries,” IMF Policy Paper (Washington).
- International Monetary Fund, 2013, “Assessing Reserve Adequacy—Further Considerations,” IMF Policy Paper (Washington).
- International Monetary Fund, 2014a, “Macroeconomic Developments in Low-Income Developing Countries,” IMF Policy Paper (Washington).
- International Monetary Fund, 2014b, Triennial Surveillance Review.

### Other Academic and Policy Literature
- Garcia, Marco, and Roberto Rigobon, 2004, “A Risk Management Approach to Emerging Market’s Sovereign Debt Sustainability with an Application to Brazilian Data,” NBER Working Paper No. 10336 (Cambridge: National Bureau of Economic Research).
- Ghosh, Atish, Jonathan Ostry, and Natalia Tamirisa, 2009, “Anticipating the Next Crisis,” Finance and Development, vol. 46, no. 3, pp 35-7.
- Kaminsky, Graciela, Saul Lizondo, and Carmen M. Reinhart, 1998, “Leading Indicators of Currency Crisis,” IMF Staff Papers, Vol. 45, No. 1, pp. 1–48.
- Lee, Jaewoo, Gian M Milesi-Ferretti, Jonathan David Ostry, Alessandro Prati, and Luca A Ricci, 2009, Exchange Rate Assessments: CGER Methodologies, IMF Occasional Paper No. 261 (Washington: International Monetary Fund).

*TNM/17/08 — International Monetary Fund, Strategy, Policy, and Review Department, 700 19th Street NW, Washington, DC 20431, USA; Tel: 1-202-623-8554; Fax: 1-202-623-6073*

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_Source: https://www.imf.org/-/media/files/publications/tnm/2017/tnm1708.pdf_