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Volatility measurement with directional change in Chinese stock market: Statistical property and investment strategy

Author

Listed:
  • Ma, Junjun
  • Xiong, Xiong
  • He, Feng
  • Zhang, Wei

Abstract

The stock price fluctuation is studied in this paper with intrinsic time perspective. The event, directional change (DC) or overshoot, are considered as time scale of price time series. With this directional change law, its corresponding statistical properties and parameter estimation is tested in Chinese stock market. Furthermore, a directional change trading strategy is proposed for invest in the market portfolio in Chinese stock market, and both in-sample and out-of-sample performance are compared among the different method of model parameter estimation. We conclude that DC method can capture important fluctuations in Chinese stock market and gain profit due to the statistical property that average upturn overshoot size is bigger than average downturn directional change size. The optimal parameter of DC method is not fixed and we obtained 1.8% annual excess return with this DC-based trading strategy.

Suggested Citation

  • Ma, Junjun & Xiong, Xiong & He, Feng & Zhang, Wei, 2017. "Volatility measurement with directional change in Chinese stock market: Statistical property and investment strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 169-180.
    • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:169-180
    DOI: 10.1016/j.physa.2016.11.113
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gong, Xu & Lin, Boqiang, 2019. "Modeling stock market volatility using new HAR-type models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 194-211.
    2. 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.
    3. Ao, Han & Li, Munan, 2024. "Exploiting the potential of a directional changes-based trading algorithm in the stock market," Finance Research Letters, Elsevier, vol. 60(C).
    4. Shengnan Li & Edward P. K. Tsang & John O'Hara, 2022. "Measuring relative volatility in high‐frequency data under the directional change approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 86-102, April.
    5. Hu, Shicheng & Zhang, Weijie & Li, Danping & Wu, Bing, 2023. "Incorporating improved directional change and regime change detection to formulate trading strategies in foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).

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