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Forecasting stock volatility during the stock market crash period: The role of Hawkes process

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  • Fan, Lina
  • Yang, Hao
  • Zhai, Jia
  • Zhang, Xiaotao

Abstract

We use a heterogeneous autoregressive model with Hawkes process (HAR-RV-H) to forecast the volatility of 300 major individual stocks in the Chinese stock market during the 2015 market crash period. The Hawkes intensity process is calculated with the tick-by-tick data of individual stocks. We show that the Hawkes indicator has predictive power for most individual stocks in the market crash period. We compare the in- and out-of-sample forecast results for the HAR type models and conclude that the Hawkes indicator can improve both in- and out-of-sample forecasting abilities.

Suggested Citation

  • Fan, Lina & Yang, Hao & Zhai, Jia & Zhang, Xiaotao, 2023. "Forecasting stock volatility during the stock market crash period: The role of Hawkes process," Finance Research Letters, Elsevier, vol. 55(PA).
    • Handle: RePEc:eee:finlet:v:55:y:2023:i:pa:s154461232300212x
    DOI: 10.1016/j.frl.2023.103839
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    as
    1. Feng Ma & Yaojie Zhang & M. I. M. Wahab & Xiaodong Lai, 2019. "The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(5), pages 400-414, August.
    2. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
    3. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
    4. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    5. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    6. Bowsher, Clive G., 2007. "Modelling security market events in continuous time: Intensity based, multivariate point process models," Journal of Econometrics, Elsevier, vol. 141(2), pages 876-912, December.
    7. E. Bacry & S. Delattre & M. Hoffmann & J. F. Muzy, 2013. "Modelling microstructure noise with mutually exciting point processes," Quantitative Finance, Taylor & Francis Journals, vol. 13(1), pages 65-77, January.
    8. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    9. Choudhry, Taufiq & Papadimitriou, Fotios I. & Shabi, Sarosh, 2016. "Stock market volatility and business cycle: Evidence from linear and nonlinear causality tests," Journal of Banking & Finance, Elsevier, vol. 66(C), pages 89-101.
    10. Giacomo Bormetti & Lucio Maria Calcagnile & Michele Treccani & Fulvio Corsi & Stefano Marmi & Fabrizio Lillo, 2015. "Modelling systemic price cojumps with Hawkes factor models," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1137-1156, July.
    11. Li, Wanli & Su, Yueying & Wang, Kaixiu, 2022. "How does economic policy uncertainty affect cross-border M&A: Evidence from Chinese firms," Emerging Markets Review, Elsevier, vol. 52(C).
    12. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    13. Peizhi Zhao & Yuyan Wang, 2022. "How Does Economic Policy Uncertainty Affect Momentum Returns? Evidence from China," IJFS, MDPI, vol. 10(3), pages 1-18, July.
    14. Feng He & Libo Yin, 2021. "Shocks to the equity capital ratio of financial intermediaries and the predictability of stock return volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 945-962, September.
    15. Bollerslev, Tim & Kretschmer, Uta & Pigorsch, Christian & Tauchen, George, 2009. "A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects," Journal of Econometrics, Elsevier, vol. 150(2), pages 151-166, June.
    16. Dimos S. Kambouroudis & David G. McMillan & Katerina Tsakou, 2016. "Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(12), pages 1127-1163, December.
    17. Ma, Feng & Liao, Yin & Zhang, Yaojie & Cao, Yang, 2019. "Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 40-55.
    18. Lang, Qiaoqi & Lu, Xinjie & Ma, Feng & Huang, Dengshi, 2022. "Oil futures volatility predictability: Evidence based on Twitter-based uncertainty," Finance Research Letters, Elsevier, vol. 47(PA).
    19. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    20. Guo, Yangli & He, Feng & Liang, Chao & Ma, Feng, 2022. "Oil price volatility predictability: New evidence from a scaled PCA approach," Energy Economics, Elsevier, vol. 105(C).
    21. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    22. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    23. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    24. Emmanuel Bacry & Sylvain Delattre & Marc Hoffmann & Jean-François Muzy, 2013. "Modelling microstructure noise with mutually exciting point processes," Post-Print hal-01313995, HAL.
    25. Clements, Adam & Liao, Yin, 2017. "Forecasting the variance of stock index returns using jumps and cojumps," International Journal of Forecasting, Elsevier, vol. 33(3), pages 729-742.
    26. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    27. Marcello Rambaldi & Emmanuel Bacry & Fabrizio Lillo, 2017. "The role of volume in order book dynamics: a multivariate Hawkes process analysis," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 999-1020, July.
    28. Jing Hao & Feng He & Feng Ma & Tong Fu, 2023. "Trading around the clock: Revisit volatility spillover between crude oil and equity markets in different trading sessions," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(6), pages 771-791, June.
    29. Yaming Ma & Ziwei Wang & Feng He, 2022. "How do economic policy uncertainties affect stock market volatility? Evidence from G7 countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2303-2325, April.
    30. Aganin, Artem, 2017. "Forecast comparison of volatility models on Russian stock market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 48, pages 63-84.
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