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.

中文翻译：

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信用投资组合中的传染性违约：贝叶斯网络方法

金融机构和监管机构对信贷组合模型的稳健性非常感兴趣，因为错误指定的模型会导致资本缓冲不足和金融体系容易发生危机。在本文中，作者提出了一种基于 Merton 模型通过结合传染效应来增强信用组合模型的方法。虽然在大多数模型中，与金融关联性相关的风险都被忽略了，但作者使用贝叶斯网络方法来揭示信用之间的直接和间接关系，同时保持因子模型的方便表示。研究和评估了一系列从真实信用违约掉期数据中学习金融网络结构和参数的技术。他们的方法在程式化的投资组合中得到了详细展示，