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626. Modeling Systemic Risk with Markov Switching Graphical SUR Models

by Daniele Bianchi, Monica Billio, Roberto Casarin, and Massimo Guidolin

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 

 


Last updated July 31, 2018