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

Amar Rao1, Marco Tedeschi2, Kamel Si Mohammed3,4, Umer Shahzad5,6
1BML Munjal University, Gurugram, India
2Department of Economic and Social Sciences, Marche Polytechnic University, Ancona, Italy
3Université de Lorraine, CEREFIGE, Metz, France
4University of Ain Temouchent, Ain Temouchent, Algeria
5Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon
6Department of Trade and Finance, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic

Tóm tắt

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.

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