This paper aims to accurately forecast stock return volatility based on a robust regression model. The robust regression model is developed by replacing the mean squared error (MSE) in the autoregressive (AR) model with the Huber loss function, and the resulting model is called the ARH model. The empirical results show that the ARH model displays significantly stronger predictive power than the AR benchmark model for different evaluation periods and forecasting horizons. From an asset allocation perspective, a mean–variance investor can obtain sizeable utility gains based on the volatility forecasts produced by the ARH model. Furthermore, we find that the superior performance of the ARH model comes from assigning small weights for the extreme values, which are mainly found during recessions and periods of high volatility. Finally, our results are robust to various settings.

中文翻译：

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使用稳健的回归模型预测股票回报波动

本文旨在基于稳健的回归模型准确预测股票收益波动。鲁棒回归模型是通过用 Huber 损失函数替换自回归 (AR) 模型中的均方误差 (MSE) 而开发的，所得模型称为 ARH 模型。实证结果表明，对于不同的评估期和预测范围，ARH 模型显示出明显强于 AR 基准模型的预测能力。从资产配置的角度来看，均值-方差投资者可以根据 ARH 模型产生的波动率预测获得可观的效用收益。此外，我们发现 ARH 模型的优越性能来自于为极端值分配较小的权重，这主要发生在经济衰退和高波动时期。最后，