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Interconnected risk contributions: an heavy-tail approach to analyse US financial sectors


  • M. Bernardi
  • L. Petrella


In this paper we consider a multivariate model-based approach to measure the dynamic evolution of tail risk interdependence among US banks, financial services and insurance sectors. To deeply investigate the risk contribution of insurers we consider separately life and non-life companies. To achieve this goal we apply the multivariate student-t Markov Switching model and the Multiple-CoVaR (CoES) risk measures introduced in Bernardi et. al. (2013b) to account for both the known stylised characteristics of the data and the contemporaneous joint distress events affecting financial sectors. Our empirical investigation finds that banks appear to be the major source of risk for all the remaining sectors, followed by the financial services and the insurance sectors, showing that insurance sector significantly contributes as well to the overall risk. Moreover, we find that the role of each sector in contributing to other sectors distress evolves over time accordingly to the current predominant financial condition, implying different interconnection strength.

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  • M. Bernardi & L. Petrella, 2014. "Interconnected risk contributions: an heavy-tail approach to analyse US financial sectors," Papers 1401.6408,, revised Apr 2014.
  • Handle: RePEc:arx:papers:1401.6408

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    References listed on IDEAS

    1. Bernal, Oscar & Gnabo, Jean-Yves & Guilmin, Grégory, 2014. "Assessing the contribution of banks, insurance and other financial services to systemic risk," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 270-287.
    2. Adams, Zeno & Füss, Roland & Gropp, Reint, 2014. "Spillover Effects among Financial Institutions: A State-Dependent Sensitivity Value-at-Risk Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(03), pages 575-598, June.
    3. Scott E. Harrington, 2009. "The Financial Crisis, Systemic Risk, and the Future of Insurance Regulation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(4), pages 785-819.
    4. John Geweke & Gianni Amisano, 2011. "Hierarchical Markov normal mixture models with applications to financial asset returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 1-29, January/F.
    5. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    6. Girardi, Giulio & Tolga Ergün, A., 2013. "Systemic risk measurement: Multivariate GARCH estimation of CoVaR," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3169-3180.
    7. Jan Bulla, 2010. "Hidden Markov models with t components. Increased persistence and other aspects," Quantitative Finance, Taylor & Francis Journals, vol. 11(3), pages 459-475.
    8. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
    9. Hua Chen & J. David Cummins & Krupa S. Viswanathan & Mary A. Weiss, 2014. "Systemic Risk and the Interconnectedness Between Banks and Insurers: An Econometric Analysis," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(3), pages 623-652, September.
    10. N. Podlich & M. Wedow, 2013. "Are insurers SIFIs? A MGARCH model to measure interconnectedness," Applied Economics Letters, Taylor & Francis Journals, vol. 20(7), pages 677-681, May.
    11. Markose, Sheri & Giansante, Simone & Shaghaghi, Ali Rais, 2012. "‘Too interconnected to fail’ financial network of US CDS market: Topological fragility and systemic risk," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 627-646.
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    Cited by:

    1. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733,, revised Jan 2016.

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