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Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators

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  • Tianlun Fei
  • Xiaoquan Liu
  • Conghua Wen

Abstract

In this paper, we study the predictive performance of two kinds of volatility estimators: the realized volatility (RV) type and duration‐based ones. This is motivated by the theoretical and empirical support for these distinct estimators. We use intraday data for 218 component stocks of the CSI 300 index in the Chinese equity market from 2010–2019 and perform in‐ and out‐of‐sample 1‐, 5‐, and 22‐day ahead volatility forecasts from combinations of volatility models and these estimators. We show that, although empirically more efficient with the US data, the duration‐based estimators fail to compete statistically, or in terms of economic value, with RV‐type ones in the Chinese market. We perform a comprehensive set of simulations to rationalize these results and show that duration‐based estimators underperform as they cannot handle the occasional heightened level of volatility in the Chinese market.

Suggested Citation

  • Tianlun Fei & Xiaoquan Liu & Conghua Wen, 2023. "Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1594-1621, November.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:7:p:1594-1621
    DOI: 10.1002/for.2974
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    as
    1. Brogaard, Jonathan & Li, Dan & Xia, Ying, 2017. "Stock liquidity and default risk," Journal of Financial Economics, Elsevier, vol. 124(3), pages 486-502.
    2. Corsi, Fulvio & Pirino, Davide & Renò, Roberto, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Journal of Econometrics, Elsevier, vol. 159(2), pages 276-288, December.
    3. Ying Jiang & Yi Cao & Xiaoquan Liu & Jia Zhai, 2019. "Volatility modeling and prediction: the role of price impact," Quantitative Finance, Taylor & Francis Journals, vol. 19(12), pages 2015-2031, December.
    4. 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.
    5. Lei Feng & Mark Seasholes, 2005. "Do Investor Sophistication and Trading Experience Eliminate Behavioral Biases in Financial Markets?," Review of Finance, Springer, vol. 9(3), pages 305-351, September.
    6. Andersen, Torben G. & Bollerslev, Tim & Meddahi, Nour, 2011. "Realized volatility forecasting and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 220-234, January.
    7. Qi Chen & Itay Goldstein & Wei Jiang, 2007. "Price Informativeness and Investment Sensitivity to Stock Price," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 619-650.
    8. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    9. Lei Feng & Mark S. Seasholes, 2005. "Do Investor Sophistication and Trading Experience Eliminate Behavioral Biases in Financial Markets?," Review of Finance, European Finance Association, vol. 9(3), pages 305-351.
    10. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    11. Owen A. Lamont & Jeremy C. Stein, 2006. "Investor Sentiment and Corporate Finance: Micro and Macro," American Economic Review, American Economic Association, vol. 96(2), pages 147-151, May.
    12. Jiqian Wang & Feng Ma & M.I.M. Wahab & Dengshi Huang, 2021. "Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 921-941, August.
    13. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    14. Yao, Juan & Ma, Chuanchan & He, William Peng, 2014. "Investor herding behaviour of Chinese stock market," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 12-29.
    15. O. E. Barndorff-Nielsen & P. Reinhard Hansen & A. Lunde & N. Shephard, 2009. "Realized kernels in practice: trades and quotes," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 1-32, November.
    16. Patton, Andrew J. & Sheppard, Kevin, 2009. "Optimal combinations of realised volatility estimators," International Journal of Forecasting, Elsevier, vol. 25(2), pages 218-238.
    17. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    18. Girardin, Eric & Liu, Zhenya, 2005. "Bank credit and seasonal anomalies in China's stock markets," China Economic Review, Elsevier, vol. 16(4), pages 465-483.
    19. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
    20. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    21. Anderson, Heather M. & Vahid, Farshid, 2007. "Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 76-90, January.
    22. Kim, Kenneth A. & Nofsinger, John R., 2008. "Behavioral finance in Asia," Pacific-Basin Finance Journal, Elsevier, vol. 16(1-2), pages 1-7, January.
    23. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    24. Lee, Bong Soo & Li, Wei & Wang, Steven Shuye, 2010. "The dynamics of individual and institutional trading on the Shanghai Stock Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 18(1), pages 116-137, January.
    25. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    26. 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.
    27. Dashan Huang & Fuwei Jiang & Jun Tu & Guofu Zhou, 2015. "Investor Sentiment Aligned: A Powerful Predictor of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 791-837.
    28. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    29. Tse, Yiu-Kuen & Dong, Yingjie, 2014. "Intraday periodicity adjustments of transaction duration and their effects on high-frequency volatility estimation," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 352-361.
    30. Bart Frijns & Aaron Gilbert & Alireza Tourani‐Rad, 2008. "Insider Trading, Regulation, And The Components Of The Bid–Ask Spread," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 31(3), pages 225-246, September.
    31. Carpenter, Jennifer N. & Lu, Fangzhou & Whitelaw, Robert F., 2021. "The real value of China’s stock market," Journal of Financial Economics, Elsevier, vol. 139(3), pages 679-696.
    32. Ji‐Eun Choi & Dong Wan Shin, 2018. "Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 691-704, September.
    33. Peng, Winnie Qian & Wei, K.C. John & Yang, Zhishu, 2011. "Tunneling or propping: Evidence from connected transactions in China," Journal of Corporate Finance, Elsevier, vol. 17(2), pages 306-325, April.
    34. Lei Gao & Gerhard Kling, 2005. "Calendar Effects in Chinese Stock Market," Annals of Economics and Finance, Society for AEF, vol. 6(1), pages 75-88, May.
    35. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    36. Wang, Xunxiao & Wu, Chongfeng & Xu, Weidong, 2015. "Volatility forecasting: The role of lunch-break returns, overnight returns, trading volume and leverage effects," International Journal of Forecasting, Elsevier, vol. 31(3), pages 609-619.
    37. Ghysels, Eric & Sinko, Arthur, 2011. "Volatility forecasting and microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 257-271, January.
    Full references (including those not matched with items on IDEAS)

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