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Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market

Author

Listed:
  • Conghua Wen
  • Fei Jia
  • Jianli Hao

Abstract

Purpose - Using intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300). Design/methodology/approach - The authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI. Findings - The empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics. Originality/value - The study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

Suggested Citation

  • Conghua Wen & Fei Jia & Jianli Hao, 2020. "Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market," China Finance Review International, Emerald Group Publishing Limited, vol. 13(2), pages 285-303, November.
    • Handle: RePEc:eme:cfripp:cfri-05-2020-0049
    DOI: 10.1108/CFRI-05-2020-0049
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    Citations

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    Cited by:

    1. Guo, Xiaozhu & Huang, Dengshi & Li, Xiafei & Liang, Chao, 2023. "Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 672-693.
    2. Chen, Zhonglu & Zhang, Li & Weng, Chen, 2023. "Does climate policy uncertainty affect Chinese stock market volatility?," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 369-381.
    3. Gao, Shang & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2023. "Forecasting stock market volatility: The sum of the parts is more than the whole," Finance Research Letters, Elsevier, vol. 55(PA).
    4. Liu, Jing & Chen, Zhonglu, 2023. "How do stock prices respond to the leading economic indicators? Analysis of large and small shocks," Finance Research Letters, Elsevier, vol. 51(C).

    More about this item

    Keywords

    Realized volatility; Volatility forecasting; HAR model; Trading behavior; Equity futures; G13; G15; G17;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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