Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach

Marta Ramos González, Antonio Partal Ureña and Pilar Gómez Fernández-Aguado

Research in International Business and Finance, 2023, vol. 64, issue C

Abstract: The economic onslaught of the COVID-19 pandemic has compromised the risk management of financial institutions. The consequences related to such an unprecedented situation are difficult to foresee with certainty using traditional methods. The regulatory credit loss attached to defaulted mortgages, so-called expected loss best estimate (ELBE), is forecasted using a machine learning technique. The projection of two ELBEs for 2022 and their comparison are presented. One accounts for the outbreak's impact, and the other presumes the nonexistence of the pandemic. Then, it is concluded that the referred crisis surely adversely affects said high-risk portfolios. The proposed method has excellent performance and may serve to estimate future expected and unexpected losses amidst any event of extraordinary magnitude.

Keywords: Machine learning; COVID-19; Internal-rating-based; Credit risk; Defaulted exposures (search for similar items in EconPapers)
JEL-codes: C53 D81 G17 G21 G28 G32 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:64:y:2023:i:c:s0275531923000338

DOI: 10.1016/j.ribaf.2023.101907

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