Intertemporal defaulted bond recoveries prediction via machine learning

DOI10.1016/j.ejor.2021.06.047zbMath1490.91230OpenAlexW3174463279MaRDI QIDQ2060436

Friedrich Baumann, Abdolreza Nazemi, Frank J. Fabozzi

Publication date: 13 December 2021

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2021.06.047

zbMATH Keywords

financemachine learningrisk managementrecovery ratesnews-based analysispower expectation propagation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Corporate finance (dividends, real options, etc.) (91G50) Credit risk (91G40)

Cites Work

• Improving corporate bond recovery rate prediction using multi-factor support vector regressions
• Local volatility and the recovery rate of credit default swaps
• Fuzzy decision fusion approach for loss-given-default modeling
• Loss functions for loss given default model comparison
• Support vector regression for loss given default modelling
• Intertemporal Forecasts of Defaulted Bond Recoveries and Portfolio Losses*
• Unnamed Item