Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach.
Res Int Bus Finance
; 64: 101907, 2023 Jan.
Article
in English
| MEDLINE | ID: covidwho-2241061
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
Language:
English
Journal:
Res Int Bus Finance
Year:
2023
Document Type:
Article
Affiliation country:
J.ribaf.2023.101907
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