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Hierarchical Graphical Models, With Application to Systemic Risk

Daniel Felix Ahelegbey () and Paolo Giudici ()

No 2014:01, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

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 graphical Gaussian 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 on-going banking union process in Europe. From a computational viewpoint, we develop a novel Markov Chain Monte Carlo algorithmbased on Bayes factor thresholding.

Pages: 33
Date: 2014
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