A Bayesian Methodology for Systemic Risk Assessment in Financial Networks

42 Pages Posted: 21 Mar 2015 Last revised: 3 May 2016

See all articles by Axel Gandy

Axel Gandy

Imperial College London - Department of Mathematics

Luitgard A. M. Veraart

London School of Economics & Political Science (LSE) - Department of Mathematics

Date Written: May 3, 2016

Abstract

We develop a Bayesian methodology for systemic risk assessment in financial networks such as the interbank market. Nodes represent participants in the network and weighted directed edges represent liabilities. Often, for every participant, only the total liabilities and total assets within this network are observable. However, systemic risk assessment needs the individual liabilities. We propose a model for the individual liabilities, which, following a Bayesian approach, we then condition on the observed total liabilities and assets and, potentially, on certain observed individual liabilities. We construct a Gibbs sampler to generate samples from this conditional distribution. These samples can be used in stress testing, giving probabilities for the outcomes of interest. As one application we derive default probabilities of individual banks and discuss their sensitivity with respect to prior information included to model the network. An R-package implementing the methodology is provided.

Keywords: Unknown interbank liabilities, systemic risk, Gibbs sampler

JEL Classification: C15, C63, C88, D85, E58, G01, G21

Suggested Citation

Gandy, Axel and Veraart, Luitgard A. M., A Bayesian Methodology for Systemic Risk Assessment in Financial Networks (May 3, 2016). Available at SSRN: https://ssrn.com/abstract=2580869 or http://dx.doi.org/10.2139/ssrn.2580869

Axel Gandy

Imperial College London - Department of Mathematics ( email )

South Kensington Campus
Imperial College
LONDON, SW7 2AZ
United Kingdom

Luitgard A. M. Veraart (Contact Author)

London School of Economics & Political Science (LSE) - Department of Mathematics ( email )

Houghton Street
GB-London WC2A 2AE
United Kingdom

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