hero working papers

Modeling Systemic Risk with Markov Switching Graphical SUR Models

Number: 626
Year: 2018
Author(s): 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