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Modeling Systemic Risk with Markov Switching Graphical SUR Models

Daniele Bianchi · Monica Billio · Roberto Casarin · Massimo Guidolin

72 citations0/0 files storedArchivio istituzionale della Ricerca - Bocconi

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Financial Risk and Volatility Modeling Banking stability, regulation, efficiency Complex Systems and Time Series Analysis

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 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.

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References

1993Common risk factors in the returns on stocks and bonds 1938A NEW MEASURE OF RANK CORRELATION 1973An Intertemporal Capital Asset Pricing Model 2014On the network topology of variance decompositions: Measuring the connectedness of financial firms 2002No Contagion, Only Interdependence: Measuring Stock Market Comovements 2009Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market 1972Factoring and weighting approaches to status scores and clique identification 2012Econometric measures of connectedness and systemic risk in the finance and insurance sectors

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