Quantifying Systemic Risk Using Bayesian Networks

Sumit Sourabh, Markus Hofer, D. Kandhai
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引用次数: 3

Abstract

We develop a novel framework using Bayesian networks to capture distress dependence in the context of counterparty credit risk. This allows us to calibrate the probability of distress of an entity conditional on the distress of a different entity. We apply our methodology to wrong-way risk model proposed by Turlakov and stress scenario testing. Our results show that stress propagation in an interconnected financial system can have a significant impact on counterparty credit exposures.
使用贝叶斯网络量化系统风险
我们开发了一个使用贝叶斯网络的新框架来捕捉交易对手信用风险背景下的困境依赖。这使我们能够以另一个实体的困境为条件来校准一个实体的困境概率。我们将我们的方法应用于Turlakov提出的错误风险模型和压力情景测试。我们的研究结果表明,在相互关联的金融体系中,压力传播会对交易对手的信用敞口产生重大影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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