Energy Pricing during the COVID-19 Pandemic: Predictive Information-Based Uncertainty Indexes with Machine Learning Algorithm

Olusanya Olubusoye, Olalekan J. Akintande, Olaoluwa Yaya, Ahamuefula Ogbonna and Adeola F. Adenikinju

MPRA Paper from University Library of Munich, Germany

Abstract: The study investigates the impact of uncertainties on energy pricing during the COVID-19 pandemic using five uncertainty measures that include the COVID-Induced Uncertainty (CIU), Economic Policy Uncertainty (EPU), Global Fear Index (GFI); Volatility Index (VIX), and the Misinformation Index of Uncertainty (MIU). The data, which span between 2-January, 2020 and 19-January, 2021, corresponding to the period of the COVID-19 pandemic. The study finds energy prices to respond significantly to the examined uncertainty measures, with EPU seen to affect the prices of most energy types during the pandemic. We also find predictive potentials inherent in VIX, CIU, and MIU for global energy sources.

Keywords: Coronavirus pandemic; Energy market; Machine Learning; Uncertainty (search for similar items in EconPapers)
JEL-codes: D8 D81 Q41 (search for similar items in EconPapers)
Date: 2021-09-21
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ene
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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