Risk aversion and the predictability of crude oil market volatility: A forecasting experiment with random forests

Abstract

We analyze the predictive power of time-varying risk aversion for the realized volatility of crude oil returns based on high-frequency data. While the popular linear heterogeneous autoregressive realized volatility (HAR-RV) model fails to recognize the predictive power of risk aversion over crude oil volatility, we find that risk aversion indeed improves forecast accuracy at all forecast horizons when we compute forecasts by means of random forests. The predictive power of risk aversion is robust to various covariates including realized skewness and realized kurtosis, various measures of jump intensity and leverage. The findings highlight the importance of accounting for nonlinearity in the data-generating process for forecast accuracy as well as the predictive power of non-cashflow factors over commodity-market uncertainty with significant implications for the pricing and forecasting in these markets.