This paper aims to address the predictor choice issue in forecasting volatility of INE oil futures by a comprehensive comparative study with a large number of predictive variables and applying machine learning models along with their interpretability tools. The main finding is that the selection of predictors is crucial for improving volatility forecasting accuracy, but it is not always the case that including more predictive variables leads to better forecasting results, even for machine learning models. Specifically, this paper has five major findings: (1) A few variables can significantly improve forecasting accuracy independently, but their contribution is limited. (2) Increasing the number of predictors from specific categories (market sentiment indicators, crude oil futures prices from other exchanges, and energy market indicators) helps to enhance forecasting accuracy. (3) Low-frequency variables have a weak effect on improving the daily volatility. (4) Ensemble tree models perform better than traditional machine learning models based on variable selection with dynamic parameter optimization, even without much parameter tuning. The above findings still hold true under a series of robustness tests and economic value assessments. These findings provide substantial evidence for addressing the issues of model and variable choice in crude oil futures volatility forecasting.

中文翻译：

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越多越好吗？预测变量选择对​​INE石油期货波动率预测的影响

本文旨在通过对大量预测变量进行全面比较研究并应用机器学习模型及其可解释性工具来解决 INE 石油期货波动性预测中的预测器选择问题。主要发现是，预测变量的选择对于提高波动率预测准确性至关重要，但包含更多预测变量并不总是能带来更好的预测结果，即使对于机器学习模型也是如此。具体来说，本文有五个主要发现：（1）少数变量可以独立显着提高预测精度，但贡献有限。 （2）增加特定类别的预测指标（市场情绪指标、其他交易所原油期货价格、能源市场指标）有助于提高预测准确性。 (3)低频变量对改善日波动性的作用较弱。 (4)即使没有太多参数调整，集成树模型也比基于动态参数优化的变量选择的传统机器学习模型表现更好。上述发现在一系列稳健性测试和经济价值评估下仍然成立。这些发现为解决原油期货波动预测中的模型和变量选择问题提供了实质性证据。