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Evaluating The Accuracy Of Tail Risk Forecasts For Systemic Risk Measurement

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  • CHRISTIAN BROWNLEES

    (Pompeu Fabra University, Department of Economics and Business, Ramon Trias Fargas, 25-27, Barcelona, Spain)

  • GIUSEPPE CAVALIERE

    (#x2020;University of Bologna, Department of Economics, Piazza Scaravilli, 2, Bologna, Italy)

  • ALICE MONTI

    (#x2020;University of Bologna, Department of Economics, Piazza Scaravilli, 2, Bologna, Italy)

Abstract

In this paper, we address how to evaluate tail risk forecasts for systemic risk (SRISK) measurement. We propose two loss functions, the Tail Tick Loss and the Tail Mean Square Error, to evaluate, respectively, Conditional Value-at-Risk (CoVaR) and MES forecasts. We then analyse CoVaR and MES forecasts for a panel of top US financial institutions between 2000 and 2012 constructed using a set of bivariate DCC-GARCH-type models. The empirical results highlight the importance of using an appropriate loss function for the evaluation of such forecasts. Among other findings, the analysis confirms that the DCC-GJR specification provides accurate predictions for both CoVaR and MES, in particular for the riskiest group of institutions in the panel (Broker-Dealers).

Suggested Citation

  • Christian Brownlees & Giuseppe Cavaliere & Alice Monti, 2018. "Evaluating The Accuracy Of Tail Risk Forecasts For Systemic Risk Measurement," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-25, June.
    • Handle: RePEc:wsi:afexxx:v:13:y:2018:i:02:n:s2010495218500094
    DOI: 10.1142/S2010495218500094
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    as
    1. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    2. Giacomini, Raffaella & Komunjer, Ivana, 2005. "Evaluation and Combination of Conditional Quantile Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 416-431, October.
    3. Popescu, Alexandra & Turcu, Camelia, 2017. "Sovereign debt and systemic risk in the eurozone," Economic Modelling, Elsevier, vol. 67(C), pages 275-284.
    4. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    5. Escanciano, Juan Carlos & Pei, Pei, 2012. "Pitfalls in backtesting Historical Simulation VaR models," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2233-2244.
    6. Sylvain Benoît & Gilbert Colletaz & Christophe Hurlin & Christophe Pérignon, 2013. "A Theoretical and Empirical Comparison of Systemic Risk Measures," Working Papers halshs-00746272, HAL.
    7. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    8. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    9. Christoffersen, Peter & Jacobs, Kris & Ornthanalai, Chayawat & Wang, Yintian, 2008. "Option valuation with long-run and short-run volatility components," Journal of Financial Economics, Elsevier, vol. 90(3), pages 272-297, December.
    10. Huang, Xin & Zhou, Hao & Zhu, Haibin, 2009. "A framework for assessing the systemic risk of major financial institutions," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2036-2049, November.
    11. Robert Engle & Eric Jondeau & Michael Rockinger, 2015. "Systemic Risk in Europe," Review of Finance, European Finance Association, vol. 19(1), pages 145-190.
    12. Wilmar Cabrera & Jorge Hurtado & Miguel Morales & Juan Sebastián Rojas, 2014. "A Composite Indicator of Systemic Stress (CISS) for Colombia," Temas de Estabilidad Financiera 80, Banco de la Republica de Colombia.
    13. Mr. Marcus Pramor & Ms. Natalia T. Tamirisa, 2006. "Common Volatility Trends in the Central and Eastern European Currencies and the Euro," IMF Working Papers 2006/206, International Monetary Fund.
    14. 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.
    15. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    16. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    17. Yun, Jaeho & Moon, Hyejung, 2014. "Measuring systemic risk in the Korean banking sector via dynamic conditional correlation models," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 94-114.
    18. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1993. "Nonlinear Dynamic Structures," Econometrica, Econometric Society, vol. 61(4), pages 871-907, July.
    19. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    20. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    21. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    22. Christian T. Brownlees & Giampiero M. Gallo, 2008. "On Variable Selection for Volatility Forecasting: The Role of Focused Selection Criteria," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 513-539, Fall.
    23. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    24. Christian Brownlees & Robert F. Engle, 2017. "SRISK: A Conditional Capital Shortfall Measure of Systemic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 48-79.
    25. Guo, Hui & Neely, Christopher J., 2008. "Investigating the intertemporal risk-return relation in international stock markets with the component GARCH model," Economics Letters, Elsevier, vol. 99(2), pages 371-374, May.
    26. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836.
    27. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    28. Escanciano, Juan Carlos & Pei, Pei, 2012. "Pitfalls in backtesting Historical Simulation VaR models," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2233-2244.
    29. Li, Dandan & Ghoshray, Atanu & Morley, Bruce, 2012. "Measuring the risk premium in uncovered interest parity using the component GARCH-M model," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 167-176.
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    3. Maarten R.C. Van Oordt, 2023. "Calibrating the Magnitude of the Countercyclical Capital Buffer Using Market‐Based Stress Tests," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(2-3), pages 465-501, March.

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