HIERARCHICAL GRAPHICAL MODELS, WITH APPLICATION TO SYSTEMIC RISK

Daniel Felix Ahelegbey and Paolo Giudici ()
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Paolo Giudici: Department of Economics and Management, University of Pavia

No 63, DEM Working Papers Series from University of Pavia, Department of Economics and Management

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 algorithm based on Bayes factor thresholding.

Keywords: Applied Bayesian models; Graphical Gaussian Models; Systemic financial risk (search for similar items in EconPapers)
Pages: 32 pages
Date: 2014-01
New Economics Papers: this item is included in nep-ban, nep-ecm and nep-rmg
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Citations: View citations in EconPapers (1)

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http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0063.pdf (application/pdf)

Related works:
Working Paper: Hierarchical Graphical Models, With Application to Systemic Risk (2014)
Working Paper: Bayesian Graphical Models for Structural Vector Autoregressive Processes (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:pav:demwpp:demwp0063

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