Abstract

We analyze the predictive power of time-varying risk aversion for the realized volatility of crude oil returns based on high-frequency data. Using random forests, and their extensions to quantile random forests and extreme random forests, we show that risk aversion improves out-of-sample accuracy of realized volatility forecasts. The predictive power of risk aversion is robust to various covariates including realized skewness and realized kurtosis, various measures of jump intensity, and leverage. Our findings highlight the importance of non-cash flow factors over commodity-market uncertainty with significant implications for the pricing and forecasting in these markets.

摘要

我们基于高频数据分析了时变风险厌恶对原油收益已实现波动性的预测能力。使用随机森林及其对分位数随机森林和极端随机森林的扩展，我们表明风险规避提高了已实现波动率预测的样本外准确性。风险厌恶的预测能力对各种协变量是稳健的，包括已实现的偏度和已实现的峰度、跳跃强度的各种度量和杠杆。我们的研究结果强调了非现金流因素对商品市场不确定性的重要性，对这些市场的定价和预测具有重要意义。