Contagious Defaults in a Credit Portfolio: A Bayesian Network Approach

Abstract

Robustness of credit portfolio models is of great interest for financial institutions and regulators, since misspecified models translate to insufficient capital buffers and a crisis-prone financial system. In this paper, we propose a method to enhance credit portfolio models based on the model of Merton by incorporating contagion effects. While in most models the risks related to financial interconnectedness are neglected, we use Bayesian network methods to uncover the direct and indirect relationships between credits, while maintaining the convenient representation of factor models. A range of techniques to learn the structure and parameters of financial networks from real Credit Default Swaps (CDS) data is studied and evaluated. Our approach is demonstrated in detail in a stylized portfolio and the impact on standard risk metrics is estimated.

Keywords: Portfolio Credit Risk, Bayesian Learning, Credit Default Swaps, Default Contagion, Probabilistic Graphical Models, Network Theory

JEL Classification: C11, C15, C53, C60, C63, G11, L14