Journal of Credit Risk

Contagious defaults in a credit portfolio: a Bayesian network approach

Ioannis Anagnostou, Javier Sanchez Rivero, Sumit Sourabh and Drona Kandhai

The robustness of credit portfolio models is of great interest for financial institutions and regulators, since misspecified models translate into insufficient capital buffers and a crisis-prone financial system. In this paper, the authors 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, the authors 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 data are studied and evaluated. Their approach is demonstrated in detail in a stylized portfolio, and the impact on standard risk metrics is estimated.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to View our subscription options

You need to sign in to use this feature. If you don’t have a account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here