Extending the popular HAR model with additional information channels to forecast realized volatility of WTI futures prices, we show that machine learning-generated forecasts provide better forecasting quality and that portfolios that are constructed with these forecasts outperform their competing models resulting in economic gains. Analyzing the selection process, we show that information channels vary across forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to the significance of information channels.

中文翻译：

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基于机器学习预测原油期货价格的实际波动率

通过使用额外的信息渠道扩展流行的 HAR 模型来预测 WTI 期货价格的实际波动性，我们表明机器学习生成的预测提供了更好的预测质量，并且使用这些预测构建的投资组合优于其竞争模型，从而带来了经济收益。通过分析选择过程，我们发现信息渠道在不同的预测范围内有所不同。变量选择产生聚类，并提供证据表明信息渠道的重要性发生了结构性变化。