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Predicting sovereign credit ratings for portfolio stress testing

Author

Listed:
  • De Oliveira Campino, Jonas

    (Lead Strategic Risk Management Specialist in the Office of Risk Management, The Inter-American Development Bank, USA)

  • Galizia, Federico

    (Chief Risk Officer, The Inter-American Development Bank, USA)

  • Serrano, Daniela

    (Economist, The Inter-American Development Bank, USA)

  • Sperling, Frank

    (Unit Chief, The Inter-American Development Bank, USA)

Abstract

This paper analyses the relationship between macroeconomic and credit cycles. It is not a straightforward relationship, particularly in sovereign credit assessment. Modelling such a relationship requires blending scenario analysis and stress testing, together with dynamic modelling of macroeconomic and credit variables. The novelty of the presented approach is its ability to cross-pollinate machine learning and Monte Carlo (MC) simulation as part of a process that overcomes the challenges faced by risk managers. The result is a probabilistic forward-looking view of credit risk scenarios that can guide action. Sovereign credit ratings are expert opinions based on relevant macroeconomic, financial and policy information. We introduce a predictive machine learning model of sovereign credit ratings that lends itself naturally to MC simulations and stress testing. The Least Absolute Shrinkage and Selection Operator (LASSO) allows considering many variables simultaneously in a nonlinear fashion as candidates for predicting sovereign ratings. The portfolio stress testing capability comes in by augmenting the set of variables used in the MC simulations to include external shock variables common to the sovereigns in the portfolio, for example, relevant global commodity prices. The resulting rating distribution can be used to calculate different relevant risk metrics, including credit-sensitive measures of risk-weighted assets.

Suggested Citation

  • De Oliveira Campino, Jonas & Galizia, Federico & Serrano, Daniela & Sperling, Frank, 2021. "Predicting sovereign credit ratings for portfolio stress testing," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 14(3), pages 229-241, June.
    • Handle: RePEc:aza:rmfi00:y:2021:v:14:i:3:p:229-241
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    More about this item

    Keywords

    capital adequacy; sovereign risk; credit rating; stress testing; machine learning; LASSO; Monte Carlo simulation;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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