A Bayesian Methodology for Systemic Risk Assessment in Financial Networks

• 成果类型: Article
• 署名作者: Gandy, Axel; Veraart, Luitgard A. M.
• 署名单位: Imperial College London; University of London; London School Economics & Political Science
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2016.2546
• 发表日期: 2017
• 页码: 4428-4446
• 关键词: Financial network; unknown interbank liabilities; Systemic risk; BAYES; mcmc; GIBBS SAMPLER; power law

摘要

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.