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Cover Technical Notes and Manuals
How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning
International Monetary Fund. Strategy, Policy, & Review Department
The IMF’s Vulnerability Exercise (VE) is a cross-country exercise that identifies country-specific near-term macroeconomic risks. As a key element of the Fund’s broader risk architecture, the VE is a bottom-up, multi-sectoral approach to risk assessments for all IMF member countries. The VE modeling toolkit is regularly updated in response to global economic developments and the latest modeling innovations. The new generation of VE models presented here leverages machine-learning algorithms. The models can better capture interactions between different parts of the economy and non-linear relationships that are not well measured in ”normal times.” The performance of machine-learning-based models is evaluated against more conventional models in a horse-race format. The paper also presents direct, transparent methods for communicating model results.
Keywords:
Risk Assessment; Supervised Machine Learning; Prediction; Sudden Stop; Exchange Market Pressure; Fiscal Crisis; Debt; Financial Crisis; Economic Crisis; Economic Growth; VE modeling toolkit; ML technique; Vulnerability Exercise approach using machine learning; IMF's Vulnerability Exercise; ML tool; crisis risk indices; Early warning systems; Global financial crisis of 2008-2009; Sudden stops; Banking crises; Global
Cover Technical Notes and Manuals
How to Assess Country Risk
The Vulnerability Exercise Approach Using Machine Learning
International Monetary Fund. Strategy, Policy, & Review Department

are typically preceded by fiscal crises; by contrast, in AEs, real sector crises could come hand-in-hand with financial crises, triggering subsequent crises in real and external sectors. Figure 29 Crisis Risk Indices in Four Sectors, Ethiopia and Greece Conclusion and Next Steps The machine-learning models presented here represent the next generation of risk assessment models in the VE . Those models with the best horse race performance will underpin country risk assessments in the four sectors explored here. The results from these models are

Keywords:
crisis definition; risk index; banking sector; RF model; model result
Cover Technical Notes and Manuals
Front Matter
International Monetary Fund. Strategy, Policy, & Review Department

Importance 18. Fiscal Risk in Greece, 2009 19. Bank Crisis History and Frequency 20. Financial Sector Model Performance 21. Financial Model Variable Importance 22. Historical Evolution of Average Risk Index 23. Global-Local Variable Interaction in Financial Sector Model 24. Financial Crisis Risk Index, Iceland and Ireland, 2005 and 2007 25. Real Sector Crisis History 26. Real Sector Model Performance 27. Real Model Variable Importance 28. Nonlinear Interactions in Real Sector Model 29. Crisis Risk Indices in Four Sectors, Ethiopia and Greece

Keywords:
I. signal extraction method; macro-crisis detection; signal extraction approach; supervised machine learning; Machine learning; Early warning systems; Global