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Measuring systemic risk via GAS models and extreme value theory: Revisiting the 2007 financial crisis

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  • Gavronski, Pedro Gerhardt
  • Ziegelmann, Flavio A.

Abstract

The advent of the 2007 financial crisis showed that risk measures formulated so far did not perform as expected. This poor performance may bring serious consequences for the system stability, possibly causing very adverse effects on the banking and insurance industries. In this paper we propose a new systemic risk measure based on extreme value theory, the Financial System Dependence Index (FSDI) which uses the spread of Credit Default Swaps (CDS) of financial institutions as the data source. Furthermore we add time dynamics for this measure, which is described by a GAS model. We motivate the quality of FSDI by comparing it to the risk measure proposed by Segoviano and Goodhart (2009), the Bank Stability Index (BSI), through a horse race based on the ideas of Rodríguez-Moreno and Peña (2013). In our empirical analysis, FSDI outperformed BSI.

Suggested Citation

  • Gavronski, Pedro Gerhardt & Ziegelmann, Flavio A., 2021. "Measuring systemic risk via GAS models and extreme value theory: Revisiting the 2007 financial crisis," Finance Research Letters, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:finlet:v:38:y:2021:i:c:s1544612320301082
    DOI: 10.1016/j.frl.2020.101498
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    References listed on IDEAS

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    1. Dong Hwan Oh & Andrew J. Patton, 2018. "Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 181-195, April.
    2. Charles P. Kindleberger & Robert Z. Aliber, 2005. "Manias, Panics and Crashes," Palgrave Macmillan Books, Palgrave Macmillan, edition 0, number 978-0-230-62804-5, December.
    3. Gonzalo, Jesus & Granger, Clive W J, 1995. "Estimation of Common Long-Memory Components in Cointegrated Systems," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 27-35, January.
    4. Charles Goodhart & Miguel Segoviano, 2009. "Banking Stability Measures," FMG Discussion Papers dp627, Financial Markets Group.
    5. Segoviano, Miguel A. & Goodhart, Charles, 2009. "Banking stability measures," LSE Research Online Documents on Economics 24416, London School of Economics and Political Science, LSE Library.
    6. Dimitrios Bisias & Mark Flood & Andrew W. Lo & Stavros Valavanis, 2012. "A Survey of Systemic Risk Analytics," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 255-296, October.
    7. Mr. C. A. E. Goodhart & Miguel A. Segoviano, 2009. "Banking Stability Measures," IMF Working Papers 2009/004, International Monetary Fund.
    8. Mauro Bernardi & Leopoldo Catania, 2019. "Switching generalized autoregressive score copula models with application to systemic risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(1), pages 43-65, January.
    9. Bartels, Mariana & Ziegelmann, Flavio A., 2016. "Market risk forecasting for high dimensional portfolios via factor copulas with GAS dynamics," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 66-79.
    10. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    11. Freixas, Xavier & Laeven, Luc & Peydró, José-Luis, 2015. "Systemic Risk, Crises, and Macroprudential Regulation," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262028697, April.
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