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Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?

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  • Peng, Huan
  • Chen, Ruoxun
  • Mei, Dexiang
  • Diao, Xiaohua

Abstract

In this paper, we use a comprehensive look to investigate whether the G7 stock markets can contain predictive information to help in forecasting the Chinese stock market volatility. Our out-of-sample empirical results indicate the kitchen sink (HAR-RV-SK) model is able to attain better performance than the benchmark model (HAR-RV) and other models, implying that the G7 stock markets can help in predicting the one-day volatility of the Chinese stock market. Moreover, the kitchen sink strategy can beat the strategy of the simple combination forecasts. Finally, the G7 stock markets can indeed contain useful information, which can increase the accuracy forecasts of the Chinese stock market.

Suggested Citation

  • Peng, Huan & Chen, Ruoxun & Mei, Dexiang & Diao, Xiaohua, 2018. "Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 78-85.
    • Handle: RePEc:eee:phsmap:v:501:y:2018:i:c:p:78-85
    DOI: 10.1016/j.physa.2018.02.093
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    as
    1. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    2. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    3. Lai, YiHao & Tseng, Jen-Ching, 2010. "The role of Chinese stock market in global stock markets: A safe haven or a hedge?," International Review of Economics & Finance, Elsevier, vol. 19(2), pages 211-218, April.
    4. Li, Xiao-Ming & Zou, Li-Ping, 2008. "How do policy and information shocks impact co-movements of China's T-bond and stock markets?," Journal of Banking & Finance, Elsevier, vol. 32(3), pages 347-359, March.
    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. Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.
    7. 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.
    8. Barry Harrison & Winston Moore, 2012. "Forecasting Stock Market Volatility in Central and Eastern European Countries," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(6), pages 490-503, September.
    9. 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.
    10. Ma, Feng & Liu, Jing & Huang, Dengshi & Chen, Wang, 2017. "Forecasting the oil futures price volatility: A new approach," Economic Modelling, Elsevier, vol. 64(C), pages 560-566.
    11. Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.
    12. Herwartz, Helmut, 2017. "Stock return prediction under GARCH — An empirical assessment," International Journal of Forecasting, Elsevier, vol. 33(3), pages 569-580.
    13. 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.
    14. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    15. Liu, Zhichao & Ma, Feng & Long, Yujia, 2015. "High and low or close to close prices? Evidence from the multifractal volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 50-61.
    16. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    17. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    18. 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.
    19. Wei, Yu & Wang, Yudong & Huang, Dengshi, 2010. "Forecasting crude oil market volatility: Further evidence using GARCH-class models," Energy Economics, Elsevier, vol. 32(6), pages 1477-1484, November.
    20. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    21. 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.
    22. Wei, Yu, 2012. "Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5546-5556.
    23. Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
    24. Duan, Yinying & Chen, Wang & Zeng, Qing & Liu, Zhicao, 2018. "Leverage effect, economic policy uncertainty and realized volatility with regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 148-154.
    25. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
    26. 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.
    27. Gong, Xu & Lin, Boqiang, 2017. "Forecasting the good and bad uncertainties of crude oil prices using a HAR framework," Energy Economics, Elsevier, vol. 67(C), pages 315-327.
    28. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
    29. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    30. Xingguo Luo & Xuyuanda Qi, 2017. "The dynamic correlations between the G7 economies and China: Evidence from both realized and implied volatilities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(10), pages 989-1002, October.
    31. Xu, Cheng Kenneth, 2000. "The microstructure of the Chinese stock market," China Economic Review, Elsevier, vol. 11(1), pages 79-97.
    32. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
    33. Martin Laurence & Francis Cai & Sun Qian, 1997. "Weak-form Efficiency and Causality Tests in Chinese Stock Markets," Multinational Finance Journal, Multinational Finance Journal, vol. 1(4), pages 291-307, December.
    34. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37.
    35. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    36. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    37. Wang Pu & Yixiang Chen & Feng Ma, 2016. "Forecasting the realized volatility in the Chinese stock market: further evidence," Applied Economics, Taylor & Francis Journals, vol. 48(33), pages 3116-3130, July.
    38. 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.
    39. Douglas G. Santos & Flavio A. Ziegelmann, 2014. "Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 284-299, July.
    40. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    41. Alan E. H. Speight & David G. McMillan, 2004. "Daily volatility forecasts: reassessing the performance of GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 449-460.
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