This paper introduces an asymmetric robust weighted least squares (ARLS) approach to improve the forecasting performance of the heterogeneous autoregressive model for realized volatility. The ARLS approach down-weights extreme observations to limit the bad influence of outliers on the estimated parameters. Compared with existing robust regression methods, our model further takes into account the asymmetry of outliers using a class of kernel functions. Out-of-sample results show the ARLS approach can generate more accurate forecasts of the S&P 500 index realized volatility in the statistical and economic senses. The model that considers the asymmetry of outliers gains superior performance among various robust regression competitors. The forecasting improvements also hold in other international stock markets. More importantly, the source of the predictive ability of the ARLS model comes from the less biased and more efficient parameter estimation.

中文翻译：

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基于非对称鲁棒回归方法预测库存实现方差

本文引入了一种非对称鲁棒加权最小二乘（ARLS）方法来提高异质自回归模型对已实现波动率的预测性能。ARLS 方法降低极端观测值的权重，以限制异常值对估计参数的不良影响。与现有的鲁棒回归方法相比，我们的模型使用一类核函数进一步考虑了异常值的不对称性。样本外结果表明，ARLS 方法可以对标准普尔 500 指数在统计和经济意义上实现的波动性进行更准确的预测。考虑异常值不对称性的模型在各种稳健回归竞争对手中获得了优越的性能。其他国际股市的预测也有所改善。更重要的是，ARLS模型的预测能力的来源来自于偏差更小、更高效的参数估计。