Improving corporate bond recovery rate prediction using multi-factor support vector regressions

DOI10.1016/j.ejor.2018.05.024zbMath1403.91369OpenAlexW2804101929WikidataQ129795562 ScholiaQ129795562MaRDI QIDQ724157

Konstantin Heidenreich, Frank J. Fabozzi, Abdolreza Nazemi

Publication date: 25 July 2018

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

Full work available at URL: https://doi.org/10.1016/j.ejor.2018.05.024

zbMATH Keywords

kernel principal component analysissparse principal component analysisrecovery rategradient boostingleast-squares support vector regression methods

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to actuarial sciences and financial mathematics (62P05) Corporate finance (dividends, real options, etc.) (91G50)

Related Items

Fuzzy decision fusion approach for loss-given-default modeling, Robust regression under the general framework of bounded loss functions, Sparse regression for large data sets with outliers, Using favorite data to analyze asymmetric competition: machine learning models, Intertemporal defaulted bond recoveries prediction via machine learning, Unsupervised quadratic surface support vector machine with application to credit risk assessment

Uses Software

• Matlab
• dr
• SpaSM

Cites Work

• Unnamed Item
• Greedy function approximation: A gradient boosting machine.
• Modeling frailty-correlated defaults using many macroeconomic covariates
• Nonlinear principal component analysis of noisy data
• Theoretical foundations of the potential function method in pattern recognition learning
• Support vector regression for loss given default modelling