Modeling Systemic Risk with Markov Switching Graphical SUR Models

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 S&P100 constituents shows that cross-firm connectivity significantly increased over the period 1999-2003 and the financial crisis of 2008-2009. Finally, we provide evidence that firm-level centrality does not correlate with market values and is instead positively linked to realized financial losses.