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Forecasting the realized volatility of CSI 300

Author

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  • Zhou, Weijie
  • Pan, Jiao
  • Wu, Xiaoli

Abstract

According to the characteristics of realized volatility existing in Shanghai and Shenzhen 300 index (China Securities Index 300, CSI 300), the GARCH family model is introduced to describe the ARCH effect of error sequences in HAR, ARFIMA, and ARFIMAX models. Then, the HAR-GARCH family, the ARFIMA-GARCH family, and the ARFIMAX-GARCH family models set is proposed, which contains 33 kinds of models. Using the quasi maximum likelihood method, the parameters of the all models are estimated with normal (N) and skewed student t (SKST) distributions. By rolling window technology, one-step-ahead rolling prediction of realized volatility for CSI 300 is conducted. The results from prediction accuracy by model confidence set (MCS) test show that the realized volatility prediction models with skewed student t distribution possess higher precision than those of normal distribution in general. The ARFIMAX and other six models in ARFIMAX-GARCH family form the optimal prediction models set, which can forecast the realized volatility of CSI 300 with better accuracy.

Suggested Citation

  • Zhou, Weijie & Pan, Jiao & Wu, Xiaoli, 2019. "Forecasting the realized volatility of CSI 300," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    • Handle: RePEc:eee:phsmap:v:531:y:2019:i:c:s037843711931057x
    DOI: 10.1016/j.physa.2019.121799
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    Citations

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    Cited by:

    1. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2022. "A moving average heterogeneous autoregressive model for forecasting the realized volatility of the US stock market: Evidence from over a century of data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 384-400, January.
    2. Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo, 2021. "An empirical study on the role of trading volume and data frequency in volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 792-816, August.
    3. Liu, Min, 2022. "The driving forces of green bond market volatility and the response of the market to the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 288-309.
    4. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    5. Xiaojie Xu & Yun Zhang, 2023. "Neural network predictions of the high-frequency CSI300 first distant futures trading volume," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(2), pages 191-207, June.

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