A Bayesian methodology for systemic risk assessment in financial networks

File Description SizeFormat 
NetworkIncomplInf.pdfFile embargoed until 06 October 2018456.51 kBAdobe PDF    Request a copy
supplementary.pdfFile embargoed until 06 October 2018280.06 kBAdobe PDF    Request a copy
Title: A Bayesian methodology for systemic risk assessment in financial networks
Author(s): Gandy, A
Veraart, LAM
Item Type: Journal Article
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.
Publication Date: 6-Oct-2017
Date of Acceptance: 1-Apr-2016
URI: http://hdl.handle.net/10044/1/31885
DOI: https://dx.doi.org/10.1287/mnsc.2016.2546
ISSN: 1526-5501
Publisher: INFORMS (Institute for Operations Research and Management Sciences)
Start Page: 4428
End Page: 4446
Journal / Book Title: Management Science
Volume: 63
Issue: 12
Copyright Statement: This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact permissions@informs.org. Copyright © 2016, INFORMS
Keywords: Social Sciences
Science & Technology
Technology
Management
Operations Research & Management Science
Business & Economics
financial network
unknown interbank liabilities
systemic risk
Bayes
MCMC
Gibbs sampler
power law
INTERBANK MARKET
CONTAGION
MATRICES
08 Information And Computing Sciences
15 Commerce, Management, Tourism And Services
Operations Research
Publication Status: Published
Embargo Date: 2018-10-06
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



Items in Spiral are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons