Modeling systemic risk with Markov Switching Graphical SUR models
Daniele Bianchi (),
Monica Billio (),
Roberto Casarin and
Massimo Guidolin ()
Journal of Econometrics, 2019, vol. 210, issue 1, 58-74
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 (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 C58 (search for similar items in EconPapers)
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Working Paper: Modeling Systemic Risk with Markov Switching Graphical SUR Models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:210:y:2019:i:1:p:58-74
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