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Predicting Tail-related Risk Measures: The Consequences of Using GARCH Filters for non-GARCH Data

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  • Amine JALAL

    (HEC-University of Lausanne and FAME)

  • Michael ROCKINGER

    (HEC-University of Lausanne, FAME and CEPR)

Abstract

We investigate the consequences for value-at-risk and expected short-fall purposes of using a GARCH filter on various mis-specified processes. We show that careful investigation of the adequacy of the GARCH filter is necessary since under mis-specifications a GARCH filter appears to do more harm than good. Using an unconditional non filtered tail estimate appears to perform satisfactorily for dependent data with a degree of dependency corresponding to actual market conditions.

Suggested Citation

  • Amine JALAL & Michael ROCKINGER, 2004. "Predicting Tail-related Risk Measures: The Consequences of Using GARCH Filters for non-GARCH Data," FAME Research Paper Series rp115, International Center for Financial Asset Management and Engineering.
    • Handle: RePEc:fam:rpseri:rp115
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    Cited by:

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    2. James, Robert & Leung, Henry & Leung, Jessica Wai Yin & Prokhorov, Artem, 2023. "Forecasting tail risk measures for financial time series: An extreme value approach with covariates," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 29-50.
    3. J. Hambuckers & C. Heuchenne, 2017. "A robust statistical approach to select adequate error distributions for financial returns," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 137-161, January.
    4. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    5. Weigert, Florian, 2013. "Crash Aversion and the Cross-Section of Expected Stock Returns Worldwide," Working Papers on Finance 1325, University of St. Gallen, School of Finance, revised Nov 2015.
    6. H. Kaibuchi & Y. Kawasaki & G. Stupfler, 2022. "GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 22(7), pages 1277-1294, July.
    7. Ardia, David & Hoogerheide, Lennart F., 2014. "GARCH models for daily stock returns: Impact of estimation frequency on Value-at-Risk and Expected Shortfall forecasts," Economics Letters, Elsevier, vol. 123(2), pages 187-190.
    8. Bee, Marco & Dupuis, Debbie J. & Trapin, Luca, 2016. "Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 86-99.
    9. Huang, Alex YiHou, 2010. "An optimization process in Value-at-Risk estimation," Review of Financial Economics, Elsevier, vol. 19(3), pages 109-116, August.
    10. Julia S. Mehlitz & Benjamin R. Auer, 2021. "Time‐varying dynamics of expected shortfall in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 895-925, June.
    11. Alex YiHou Huang, 2010. "An optimization process in Value‐at‐Risk estimation," Review of Financial Economics, John Wiley & Sons, vol. 19(3), pages 109-116, August.
    12. Righi, Marcelo Brutti & Ceretta, Paulo Sergio, 2015. "A comparison of Expected Shortfall estimation models," Journal of Economics and Business, Elsevier, vol. 78(C), pages 14-47.
    13. Taylor, James W., 2020. "Forecast combinations for value at risk and expected shortfall," International Journal of Forecasting, Elsevier, vol. 36(2), pages 428-441.
    14. Hamidreza Arian & Hossein Poorvasei & Azin Sharifi & Shiva Zamani, 2020. "The Uncertain Shape of Grey Swans: Extreme Value Theory with Uncertain Threshold," Papers 2011.06693, arXiv.org.
    15. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2017. "Risk quantification in turmoil markets," Risk Management, Palgrave Macmillan, vol. 19(3), pages 202-224, August.
    16. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
    17. Alfonso Novales & Laura Garcia-Jorcano, 2019. "Backtesting Extreme Value Theory models of expected shortfall," Documentos de Trabajo del ICAE 2019-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    18. Marco Bee & Debbie J. Dupuis & Luca Trapin, 2016. "US stock returns: are there seasons of excesses?," Quantitative Finance, Taylor & Francis Journals, vol. 16(9), pages 1453-1464, September.
    19. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    20. Riedel, Christoph & Wagner, Niklas, 2015. "Is risk higher during non-trading periods? The risk trade-off for intraday versus overnight market returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 53-64.
    21. Quang Trinh, Vu & Duong Cao, Ngan & Li, Teng & Elnahass, Marwa, 2023. "Social capital, trust, and bank tail risk: The value of ESG rating and the effects of crisis shocks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).

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    More about this item

    Keywords

    Extreme value theory; Value at Risk (VaR); Expected shortfall; GARCH; Markov switching; Jump diffusion; Backtesting.;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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