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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.

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