A dimension reduction approach for loss valuation in credit risk modeling

Jian He, Asma Khedher and Peter Spreij
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Jian He: Korteweg–de Vries Institute, University of Amsterdam, Amsterdam, The Netherlands
Asma Khedher: Korteweg–de Vries Institute, University of Amsterdam, Amsterdam, The Netherlands
Peter Spreij: Korteweg–de Vries Institute, University of Amsterdam, Amsterdam, The Netherlands†IMAPP, Radboud University, Nijmegen, The Netherlands

International Journal of Financial Engineering (IJFE), 2024, vol. 11, issue 01, 1-48

Abstract: This paper addresses the “curse of dimensionality†in the loss valuation of credit risk models. A dimension reduction methodology based on the Bayesian filter and smoother is proposed. This methodology is designed to achieve a fast and accurate loss valuation algorithm in credit risk modeling, but it can also be extended to valuation models of other risk types. The proposed methodology is generic, robust and can easily be implemented. Moreover, the accuracy of the proposed methodology in the estimation of expected loss and value-at-risk (VaR) is illustrated by numerical experiments. The results suggest that, compared to the currently most used Principal Component Analysis (PCA) approach, the proposed methodology provides more accurate estimation of expected loss and VaR of a loss distribution.

Keywords: Bayesian filter; credit risk; loss valuation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S2424786323500585

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