Modeling Systemic Risk with Markov Switching Graphical SUR Models

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.

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Citation

Bianchi, Daniele, Monica Billio, Roberto Casarin, and Massimo Guidolin. 2019. “Modeling Systemic Risk with Markov Switching Graphical SUR Models.” Journal of Econometrics 210 (1): 58–74.

@article{BBCG19,
author = {Daniele Bianchi and Monica Billio and Roberto Casarin and Massimo Guidolin},
year = {2019},
title = {Modeling Systemic Risk with Markov Switching Graphical SUR Models},
journal = {Journal of Econometrics},
volume = {210},
number = {1},
pages = {58--74}}