Uncertainty and crude oil market volatility: new evidence

Chao Liang, Yu Wei, Xiafei Li, Xuhui Zhang and Yifeng Zhang

Applied Economics, 2020, vol. 52, issue 27, 2945-2959

Abstract: The main goal of this paper is to investigate the predictability of five economic uncertainty indices for oil price volatility in a changing world. We employ the standard predictive regression framework, several model combination approaches, as well as two prevailing model shrinkage methods to evaluate the performances of the uncertainty indices. The empirical results based on simple autoregression models including only one index suggest that global economic policy uncertainty (GEPU) and US equity market volatility (EMV) indices have significant predictive power for crude oil market volatility. In addition, the model combination approaches adopted in this paper can improve slightly the performances of individual autoregressive models. Lastly, the two model shrinkage methods, namely Elastin net and Lasso, outperform other individual AR-type model and combination models in most forecasting cases. Other empirical results based on alternative forecasting methods, estimation window sizes, high/low volatility and economic expansion/recession time periods further make sure the robustness of our major conclusions. The findings in this paper also have several important economic implications for oil investors.

Date: 2020
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Citations: View citations in EconPapers (101)

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DOI: 10.1080/00036846.2019.1696943

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