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Can Volatility Models Explain Extreme Events?

Author

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  • Luca Trapin

Abstract

This paper revisits several existing volatility models by the light of extremal dependence, that is, serial dependence in extreme returns. First, we investigate the extremal properties of different high-frequency-based volatility processes and show that only a subset of them can generate dependence in the extremes. Second, we corroborate the empirical evidence on extremal dependence in financial returns, showing that extreme returns present strong and persistent correlation and that extreme negative returns are much more correlated than positive ones. Finally, a large empirical analysis suggests that only models exhibiting extremal dependence and endowed with a leverage component can appropriately explain extreme events.

Suggested Citation

  • Luca Trapin, 2018. "Can Volatility Models Explain Extreme Events?," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 297-315.
    • Handle: RePEc:oup:jfinec:v:16:y:2018:i:2:p:297-315.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbx031
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Cristi Spulbar & Elena Loredana Minea, 2022. "Inefficient Stock Markets And Their Implications In The Context Of Extreme Financial Events: A Theoretical Framework," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 38-41, February.
    4. Hoga, Yannick, 2021. "The uncertainty in extreme risk forecasts from covariate-augmented volatility models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 675-686.
    5. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    6. Yong Ma & Lu Yan & Dongtao Pan, 2024. "The power of news data in forecasting tail risk: evidence from China," Empirical Economics, Springer, vol. 67(6), pages 2607-2642, December.
    7. Sakariyahu, Rilwan & Johan, Sofia & Lawal, Rodiat & Paterson, Audrey & Chatzivgeri, Eleni, 2023. "Dynamic connectedness between investors’ sentiment and asset prices: A comparison between major markets in Europe and USA," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    8. Tobias Fissler & Yannick Hoga, 2021. "Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability," Papers 2104.10673, arXiv.org, revised Feb 2022.
    9. Zhu, Bo & Lin, Renda & Liu, Jiahao, 2020. "Magnitude and persistence of extreme risk spillovers in the global energy market: A high-dimensional left-tail interdependence perspective," Energy Economics, Elsevier, vol. 89(C).
    10. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.

    More about this item

    Keywords

    extremal dependence; realized volatility; return predictability; tail risk; volatility models;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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