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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
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Konstantinos Gkillas: Department of Business Administration, University of Patras – University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece

No 201972, Working Papers from University of Pretoria, Department of Economics

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.

Keywords: Oil price; Realized volatility; Risk aversion; Random forests (search for similar items in EconPapers)
JEL-codes: G17 Q02 Q47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-ene, nep-for, nep-ore, nep-rmg and nep-upt
Date: 2019-09
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