Bayesian Selection of Systemic Risk Networks

Daniel Felix Ahelegbey and Paolo Giudici

A chapter in Bayesian Model Comparison, 2014, vol. 34, pp 117-153 from Emerald Group Publishing Limited

Abstract: The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of Bayesian Gaussian graphical models. From a statistical viewpoint, we propose a new class of hierarchical Bayesian graphical models that can split correlations between institutions into country specific and idiosyncratic ones, in a way that parallels the decomposition of returns in the well-known Capital Asset Pricing Model. From a financial economics viewpoint, we suggest a way to model systemic risk that can explicitly take into account frictions between different financial markets, particularly suited to study the ongoing banking union process in Europe. From a computational viewpoint, we develop a novel Markov chain Monte Carlo algorithm based on Bayes factor thresholding.

Keywords: Bayesian analysis; financial econometrics; financial risk management; graphical Gaussian models; model construction and estimation; C58; C51; C11 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320140000034007

DOI: 10.1108/S0731-905320140000034007

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