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Risk Aversion and the Predictability of Crude Oil Market Volatility: A Forecasting Experiment with Random Forests

Author

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  • Riza Demirer

    (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA)

  • Konstantinos Gkillas

    (Department of Business Administration, University of Patras – University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)

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.

Suggested Citation

  • Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Risk Aversion and the Predictability of Crude Oil Market Volatility: A Forecasting Experiment with Random Forests," Working Papers 201972, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201972
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    Cited by:

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    3. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    4. Gupta, Rangan & Ji, Qiang & Pierdzioch, Christian & Plakandaras, Vasilios, 2023. "Forecasting the conditional distribution of realized volatility of oil price returns: The role of skewness over 1859 to 2023," Finance Research Letters, Elsevier, vol. 58(PC).

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    Keywords

    Oil price; Realized volatility; Risk aversion; Random forests;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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