Technical Notes and Manuals

How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning

May 7, 2021

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How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning, (USA: International Monetary Fund, 2021) accessed December 3, 2024

Disclaimer: This Technical Guidance Note should not be reported as representing the views of the IMF. The views expressed in this Note are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

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.

Subject: Balance of payments, Banking crises, Early warning systems, Expenditure, Financial crises, Global financial crisis of 2008-2009, Revenue administration, Sudden stops

Keywords: Banking crises, Crisis risk indices, Debt, Early warning systems, Economic Crisis, Economic Growth, Exchange Market Pressure, Financial Crisis, Fiscal Crisis, Global, Global financial crisis of 2008-2009, IMF's Vulnerability Exercise, ML technique, ML tool, Prediction, Risk Assessment, Sudden Stop, Sudden stops, Supervised Machine Learning, VE modeling toolkit, Vulnerability Exercise approach using machine learning

Publication Details

  • Pages:

    66

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Technical Notes and Manuals No. 2021/003

  • Stock No:

    TNMEA2021003

  • ISBN:

    9781513574219

  • ISSN:

    2075-8669

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