Bianchi, Daniele, Billio, Monica, Casarin, Roberto and Guidolin, Massimo (2019) Modeling systemic risk with Markov switching graphical SUR models. Journal of Econometrics, 210 (1). pp. 58-74. doi:10.1016/j.jeconom.2018.11.005

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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.

Item Type:	Journal Article
Subjects:	H Social Sciences > HB Economic Theory
Divisions:	Faculty of Social Sciences > Warwick Business School > Finance Group Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH):	Risk -- Mathematical models -- European Union countries, Markov processes, Monte Carlo method
Journal or Publication Title:	Journal of Econometrics
Publisher:	Elsevier BV * North-Holland
ISSN:	0304-4076
Official Date:	May 2019
Dates:	Date Event May 2019 Published 12 November 2018 Available 1 May 2018 Accepted
Date of first compliant deposit:	5 March 2018
Volume:	210
Number:	1
Page Range:	pp. 58-74
DOI:	10.1016/j.jeconom.2018.11.005
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID SYRTO-SSH-2012-32027 Seventh Framework Programme http://dx.doi.org/10.13039/100011102 Systemic Risk Institut Europlace de Finance UNSPECIFIED MISURA Italy. Ministero dell'educazione nazionale http://viaf.org/viaf/150573269

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