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Forecasting the Chinese stock market volatility: A regression approach with a t-distributed error

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  • Mengxi He
  • Yaojie Zhang
  • Danyan Wen
  • Yudong Wang

Abstract

In this paper, we improve the ordinary least squares (OLS) estimation approach by replacing a normally distributed error with a t-distributed error. Empirically, we investigate the predictability of the Chinese stock market volatility based on this modified approach. Results show that the modified OLS method with a t-distributed error has a significantly stronger forecasting power than its counterpart with a normally distributed error. From an asset allocation perspective, the modified OLS approach can help a mean-variance investor obtain sizeable utility gains. We also conduct two extended empirical analyses and further verify the superiority of the regression approach with a t-distributed error. Our results are robust to a series of settings. Finally, we find that the regression approach with a t-distributed error shows greater tolerance for outliers by assigning smaller weights to them, thereby highlighting its superior performance.

Suggested Citation

  • Mengxi He & Yaojie Zhang & Danyan Wen & Yudong Wang, 2022. "Forecasting the Chinese stock market volatility: A regression approach with a t-distributed error," Applied Economics, Taylor & Francis Journals, vol. 54(50), pages 5811-5826, October.
    • Handle: RePEc:taf:applec:v:54:y:2022:i:50:p:5811-5826
    DOI: 10.1080/00036846.2022.2053653
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    Cited by:

    1. Yaojie Zhang & Mengxi He & Yuqi Zhao & Xianfeng Hao, 2023. "Predicting stock realized variance based on an asymmetric robust regression approach," Bulletin of Economic Research, Wiley Blackwell, vol. 75(4), pages 1022-1047, October.
    2. Jin, Daxiang & He, Mengxi & Xing, Lu & Zhang, Yaojie, 2022. "Forecasting China's crude oil futures volatility: How to dig out the information of other energy futures volatilities?," Resources Policy, Elsevier, vol. 78(C).

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