Quantifying Systemic Risk Using Bayesian Networks

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.

Keywords: Bayesian network, Wrong-way risk, valuation adjustments, systemic risk, machine learning

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