Role of Economic Policy Uncertainty in Energy Commodities Prices Forecasting: Evidence from a Hybrid Deep Learning Approach

Amar Rao (), Marco Tedeschi (), Kamel Si Mohammed () and Umer Shahzad ()
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Amar Rao: BML Munjal University
Marco Tedeschi: Marche Polytechnic University
Kamel Si Mohammed: University of Ain Temouchent
Umer Shahzad: Department of Trade and Finance, Faculty of Economics and Management, Czech University of Life Sciences Prague

Computational Economics, 2024, vol. 64, issue 6, No 6, 3295-3315

Abstract: Abstract Amidst a dynamic energy market landscape, understanding evolving influencing factors is pivotal. Accurate forecasting techniques are indispensable for effective energy resource management. This study focuses on illuminating insights into economic uncertainty and commodity price forecasting. A meticulously curated dataset spanning January 2000 to December 2022 forms the foundation, incorporating diverse economic and financial uncertainty metrics. Through an innovative research framework, we discern influential factors and forecast their trajectories. Three deep learning models—Short-Term Memory, Gated Recurrent Units, and Multilayer Perception Network—are deployed. The Multilayer Perception model emerges as the standout, showcasing exceptional predictive capability rooted in its adeptness at decoding intricate market patterns. This finding holds significance for policymakers, industry experts, and energy economists. The Multilayer Perception model’s supremacy offers a robust tool for decision-making in crafting economic policies and navigating volatile markets.

Keywords: Oil price; Natural gas; Economic uncertainty; Deep learning; Forecast; Commodity prices (search for similar items in EconPapers)
JEL-codes: O14 Q56 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-024-10550-3

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