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Intraday volatility analysis of CSI 300 index futures: a dependent functional data method

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  • Danni Wang
  • Zhifang Su
  • Qifang Li

Abstract

This study introduces a new volatility model based on dependent functional data to investigate the intraday volatility characteristics of CSI 300 in the context of high-frequency data. The volatility curve is fitted and reconstructed using three methods: functional principal component analysis, Newey-West kernel, and truncation-free Bartlett kernel. We adopt a functional time series approach for short-term dynamic forecasting. The empirical results show that the proposed dependent functional volatility estimation model based on the long-term covariance of the truncated Bartlett kernel can accurately capture the intraday volatility trajectory and outperforms other models in terms of forecast accuracy and profitability. This study improves the volatility-related research methodology, which is conducive to discovering the price formation mechanism of the stock index futures market and improving risk management capabilities.

Suggested Citation

  • Danni Wang & Zhifang Su & Qifang Li, 2023. "Intraday volatility analysis of CSI 300 index futures: a dependent functional data method," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 36(1), pages 312-332, December.
    • Handle: RePEc:taf:reroxx:v:36:y:2023:i:1:p:312-332
    DOI: 10.1080/1331677X.2022.2076144
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