Energy pricing during the COVID-19 pandemic: Predictive information-based uncertainty indexes with machine learning algorithm

Autor: Olusanya E. Olubusoye, Olalekan J. Akintande, OlaOluwa S. Yaya, Ahamuefula E. Ogbonna, Adeola F. Adenikinju
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Intelligent Systems with Applications, Vol 12, Iss , Pp 200050- (2021)
Druh dokumentu: article
ISSN: 2667-3053
DOI: 10.1016/j.iswa.2021.200050
Popis: 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.
Databáze: Directory of Open Access Journals