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Modeling Systemic Risk with Markov Switching Graphical SUR Models

Daniele Bianchi (), Monica Billio (), Roberto Casarin and Massimo Guidolin ()

No 626, Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University

Abstract: We propose a Markov Switching Graphical Seemingly Unrelated Regression (MS-GSUR) model to investigate time-varying systemic risk based on a range of multi-factor asset pricing models. Methodologically, we develop a Markov Chain Monte Carlo (MCMC) scheme in which latent states are identified on the basis of a novel weighted eigenvector centrality measure. An empirical application to the constituents of the S&P100 index shows that cross-firm connectivity significantly increased over the period 1999-2003 and during the financial crisis in 2008-2009. Finally, we provide evidence that firm-level centrality does not correlate with market values and it is instead positively linked to realized financial losses. Keywords: Markov Regime-Switching, Weighted Eigenvector Centrality, Graphical Models, MCMC, Systemic Risk, Network Connectivity JEL codes: C11, C15, C32, C58

New Economics Papers: this item is included in nep-ecm and nep-rmg
Date: 2018
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Journal Article: Modeling systemic risk with Markov Switching Graphical SUR models (2019) Downloads
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