Quantifying Systemic Risk Using Bayesian Networks

15 Pages Posted: 23 Feb 2020

See all articles by Sumit Sourabh

Sumit Sourabh

University of Amsterdam; ING Bank - Netherlands Office

Markus Hofer

Bayerische Landesbank

Drona Kandhai

University of Amsterdam; ING Bank - Netherlands Office

Date Written: January 26, 2020

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

Suggested Citation

Sourabh, Sumit and Hofer, Markus and Kandhai, Drona, Quantifying Systemic Risk Using Bayesian Networks (January 26, 2020). Available at SSRN: https://ssrn.com/abstract=3525739 or http://dx.doi.org/10.2139/ssrn.3525739

Sumit Sourabh (Contact Author)

University of Amsterdam ( email )

Spui 21
Amsterdam, 1018 WB
Netherlands

ING Bank - Netherlands Office

1102 MG Amsterdam
P.O. Box 1800
1000 BV Amsterdam
Netherlands

Markus Hofer

Bayerische Landesbank ( email )

Brienner Str. 18
Munich
Germany

Drona Kandhai

University of Amsterdam ( email )

Spui 21
Amsterdam, 1018 WB
Netherlands

ING Bank - Netherlands Office

1102 MG Amsterdam
P.O. Box 1800
1000 BV Amsterdam
Netherlands

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