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The role of high-frequency data in volatility forecasting: evidence from the China stock market

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  • Min Liu
  • Chien-Chiang Lee
  • Wei-Chong Choo

Abstract

This research investigates the role of high-frequency data in volatility forecasting of the China stock market by particularly feeding different frequency return series directly into a large number of GARCH versions. The contributions of this research are as follows. 1) We provide clear evidence to support that the superiority of traditional time series models in volatility forecasting remains by taking advantage of high-frequency data. 2) We incorporate different distribution assumptions in GARCH models to capture the stylized facts of high-frequency data. The result shows that: 1) data frequency in GARCH application substantially influence the accuracy of volatility forecasting, as the higher the frequency is of the return series, the better are the forecasts provided; 2) non-normal distributions such as skewed student-t and generalized error distribution are more capable at reproducing the stylized facts of both intraday and daily return series than normal distribution; and 3) GARCH estimated by 5-min returns not only outperforms other GARCH alternatives, but also considerably beats RV-based models such as HAR and ARFIMA at volatility forecasting.

Suggested Citation

  • Min Liu & Chien-Chiang Lee & Wei-Chong Choo, 2021. "The role of high-frequency data in volatility forecasting: evidence from the China stock market," Applied Economics, Taylor & Francis Journals, vol. 53(22), pages 2500-2526, May.
    • Handle: RePEc:taf:applec:v:53:y:2021:i:22:p:2500-2526
    DOI: 10.1080/00036846.2020.1862747
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    Cited by:

    1. Liu, Min & Guo, Tongji & Ping, Weiying & Luo, Liangqing, 2023. "Sustainability and stability: Will ESG investment reduce the return and volatility spillover effects across the Chinese financial market?," Energy Economics, Elsevier, vol. 121(C).
    2. Liang, Chao & Xia, Zhenglan & Lai, Xiaodong & Wang, Lu, 2022. "Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model," Energy Economics, Elsevier, vol. 116(C).
    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. Zhu, Bangzhu & Wan, Chunzhuo & Wang, Ping, 2022. "Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach," Energy Economics, Elsevier, vol. 115(C).
    5. Hung, Ying-Shu & Lee, Chingnun & Chen, Pei-Fen, 2022. "China’s monetary policy and global stock markets: A new cointegration approach with smoothing structural changes," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 643-666.
    6. 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).
    7. Huang, Yirong & Luo, Yi, 2024. "Forecasting conditional volatility based on hybrid GARCH-type models with long memory, regime switching, leverage effect and heavy-tail: Further evidence from equity market," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    8. Liu, Min & Lee, Chien-Chiang, 2022. "Is gold a long-run hedge, diversifier, or safe haven for oil? Empirical evidence based on DCC-MIDAS," Resources Policy, Elsevier, vol. 76(C).

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