难以击败：机器学习时代滚动窗口被忽视的影响
HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine Learning
AI总结：
本文在1,455只股票数据集上比较HAR模型与机器学习方法的波动率预测能力，发现采用精细化滚动窗口拟合的HAR模型在QLIKE、MSE和效用指标上均优于经过超参数调优的ML模型，且计算成本更低，为HAR作为稳健基准提供了依据。
摘要：
我们在一个包含1,455只股票的前所未有的数据集上，研究了异质自回归（HAR）模型与机器学习（ML）技术的预测能力。我们的分析聚焦于拟合方案的作用，特别是训练窗口和重新估计频率在决定HAR模型表现中的作用。尽管进行了广泛的超参数调优，当HAR模型采用精细化的拟合方法时，ML模型仍未能超越HAR所设定的线性基准。此外，HAR的简洁性使得模型具有可解释性，并且计算成本大幅降低。我们使用QLIKE、MSE和已实现效用指标评估性能，发现当两者仅依赖已实现波动率和VIX作为预测因子时，HAR始终优于其ML对应模型。我们的结果强调了正确设定拟合方案的重要性。结果表明，经过适当拟合的HAR模型提供了更优的预测精度，为其实际应用和作为基准使用建立了稳健的指导原则。本研究不仅再次证实了HAR模型的有效性，还对ML方法在已实现波动率预测中的实际局限性提供了批判性视角。
英文摘要：
We investigate the predictive abilities of the heterogeneous autoregressive (HAR) model compared to machine learning (ML) techniques across an unprecedented dataset of 1,455 stocks. Our analysis focuses on the role of fitting schemes, particularly the training window and re-estimation frequency, in determining the HAR model's performance. Despite extensive hyperparameter tuning, ML models fail to surpass the linear benchmark set by HAR when utilizing a refined fitting approach for the latter. Moreover, the simplicity of HAR allows for an interpretable model with drastically lower computational costs. We assess performance using QLIKE, MSE, and realized utility metrics, finding that HAR consistently outperforms its ML counterparts when both rely solely on realized volatility and VIX as predictors. Our results underscore the importance of a correctly specified fitting scheme. They suggest that properly fitted HAR models provide superior forecasting accuracy, establishing robust guidelines for their practical application and use as a benchmark. This study not only reaffirms the efficacy of the HAR model but also provides a critical perspective on the practical limitations of ML approaches in realized volatility forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
