Contagion in financial systems: A Bayesian network approach

TL;DR

This paper introduces a probabilistic Bayesian network approach to model and analyze contagion and systemic risk in interconnected financial systems, accounting for complex cyclic linkages.

Contribution

It develops a novel structural default model using Bayesian networks, enabling detection of contagion channels and systemic importance in financial networks.

Findings

Effective characterization of joint default distributions

Identification of contagion pathways

Quantitative assessment of systemic importance

Abstract

We develop a structural default model for interconnected financial institutions in a probabilistic framework. For all possible network structures we characterize the joint default distribution of the system using Bayesian network methodologies. Particular emphasis is given to the treatment and consequences of cyclic financial linkages. We further demonstrate how Bayesian network theory can be applied to detect contagion channels within the financial network, to measure the systemic importance of selected entities on others, and to compute conditional or unconditional probabilities of default for single or multiple institutions.