Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility

Yaojie Zhang, Mengxi He, Yudong Wang and Chao Liang

International Journal of Forecasting, 2023, vol. 39, issue 3, 1318-1332

Abstract: This paper constructs an aligned global economic policy uncertainty (GEPU) index based on a modified machine learning approach. We find that the aligned GEPU index is an informative predictor for forecasting crude oil market volatility both in- and out-of-sample. Compared to general GEPU indices without supervised learning, well-recognized economic variables, and other popular uncertainty indicators, the aligned GEPU index is rather powerful and can provide preponderant or complementary information. The trading strategy based on the aligned GEPU index can also generate sizable economic gains. The statistical source of the aligned GEPU index’s predictive power is that it can learn both the magnitude and sign of national EPU variables’ predictive ability and thus yields reasonable and informative loadings. On the other hand, the economic driving force probably stems from the ability for forecasting the shocks of oil-related fundamentals.

Keywords: Global economic policy uncertainty; Crude oil market; Volatility forecasting; Supervised learning; Crude oil fundamentals (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:3:p:1318-1332

DOI: 10.1016/j.ijforecast.2022.07.002

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