Cargando…
Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach()
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...
Autores principales: | González, Marta Ramos, Ureña, Antonio Partal, Fernández-Aguado, Pilar Gómez |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9933877/ https://www.ncbi.nlm.nih.gov/pubmed/36814639 http://dx.doi.org/10.1016/j.ribaf.2023.101907 |
Ejemplares similares
-
Loss rate forecasting framework based on macroeconomic changes: Application to US credit card industry
por: Taghiyeh, Sajjad, et al.
Publicado: (2021) -
An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance
por: Belhadi, Amine, et al.
Publicado: (2021) -
Structured Credit Products: Credit Derivatives and Synthetic Securitisation
por: Choudhry, Moorad
Publicado: (2010) -
Credit derivatives and structured credit: a guide for investors
por: Bruyère, Richard, et al.
Publicado: (2006) -
Concurrent credit portfolio losses
por: Sicking, Joachim, et al.
Publicado: (2018)