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 Publishing Ltd
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)
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