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

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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.

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Data Availability

The datasets used during the current study are available from the corresponding or first author on reasonable request.

Notes

  1. Several papers investigated the relevance of economic uncertainty in the context of energy prices, see, among others, Kang and Ratti (2013), Kang et al. (2017) and Herrera et al. (2019).

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No funding was received for this research work. Authors have no sources of conflict of interest.

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Authors and Affiliations

  1. BML Munjal University, 67th KM Stone, NH-8, Gurugram,, Haryana, 122413, India

    Amar Rao

  2. Department of Economic and Social Sciences, Marche Polytechnic University, Ancona, Italy

    Marco Tedeschi

  3. University of Ain Temouchent, Ain Temouchent, Algeria

    Kamel Si Mohammed

  4. Université de Lorraine, CEREFIGE, Metz, 57000, France

    Kamel Si Mohammed

  5. Department of Trade and Finance, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic

    Umer Shahzad

  6. Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon

    Umer Shahzad

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  1. Amar Rao
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  2. Marco Tedeschi
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  4. Umer Shahzad
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Correspondence to Umer Shahzad.

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Rao, A., Tedeschi, M., Mohammed, K.S. et al. Role of Economic Policy Uncertainty in Energy Commodities Prices Forecasting: Evidence from a Hybrid Deep Learning Approach. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10550-3

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