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

dc.contributor.authorRamos González, M.
dc.contributor.authorPartal Ureña, A.
dc.contributor.authorGómez Fernández-Aguado, P.
dc.date.accessioned2024-01-08T13:18:03Z
dc.date.available2024-01-08T13:18:03Z
dc.date.issued2023
dc.description.abstractThe 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 magnitudees_ES
dc.identifier.issn0275-5319es_ES
dc.identifier.other10.1016/j.ribaf.2023.101907es_ES
dc.identifier.urihttps://hdl.handle.net/10953/1366
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.ispartofResearch in International Business and Finance, 64, 101907es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectMachine learning, COVID-19, Internal-rating-based, Credit risk Defaulted exposureses_ES
dc.subject.udcC53, G17, D81, G21, G28, G32es_ES
dc.titleForecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approaches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_ES

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