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

Riza Demirer, Konstantinos Gkillas, Rangan Gupta and Christian Pierdzioch

Journal of the Operational Research Society, 2022, vol. 73, issue 8, 1755-1767

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.

Date: 2022
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Working Paper: Risk Aversion and the Predictability of Crude Oil Market Volatility: A Forecasting Experiment with Random Forests (2019)
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DOI: 10.1080/01605682.2021.1936668

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