Predicting sovereign credit ratings for portfolio stress testing
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.
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Author's Biography
Jonas De Oliveira Campino is a lead strategic risk management specialist in the Office of Risk Management at the Inter-American Development Bank (IDB) in Washington, DC specializing in the area of capital adequacy modelling and simulations. Jonas holds a PhD in International Business Management with a minor in International Economics and an MBA in International Business Management from the George Washington University (GWU). He also holds an MA in International Trade and Investment Policy from the Elliott School of International Affairs at GWU and a BA in Economics from the University of Maryland at College Park.
Federico Galizia is the Chief Risk Officer at the Inter-American Development Bank (IDB). He advises the President, the Executive Vice President and the Board of Directors on their oversight of market, credit, socio-environmental and operational risk, in accordance with the shareholders’ triple-A mandate. A founding member of the Multilateral Development Banks (MDB) CRO Forum, he sponsors implementation of the G20 Action Plan to Optimize MDB Balance Sheets. Federico was previously Head of Risk and Portfolio Management at the European Investment Fund and Adviser to the President of the European Investment Bank. He holds a PhD in Economics from Yale University and has published and taught MBA courses in the fields of risk management and international finance.
Daniela Serrano is an economist specialised in sovereign credit risk in emerging markets. She works at the Inter-American Development Bank (IDB) in the credit risk management unit, monitoring credit rating methodologies and capital adequacy. Daniela has research experience in debt sustainability and default determinants and has a deep interest in data science. She holds an MPA in International Development from Harvard University and degrees in Economics and Political Science from Washington University in St. Louis.
Frank Sperling Unit Chief for Strategic Risk at the Inter-American Development Bank, is a leader the management of financial risk through the design and implementation of innovative solutions in the areas of capital adequacy, profitability and balance sheet management. He has an MBA in International Business for the George Washington University as well as master degrees in Engineering and Engineering Management. He holds a certification as FRM and served as regional co-director for GARP.
Citation
De Oliveira Campino, Jonas, Galizia, Federico, Serrano, Daniela and Sperling, Frank (2021, June 1). Predicting sovereign credit ratings for portfolio stress testing. In the Journal of Risk Management in Financial Institutions, Volume 14, Issue 3. https://doi.org/10.69554/RIOZ4371.
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