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

Intelligent Systems with Applications

OlaOluwa S. Yaya¹  Ahamuefula E. Ogbonna²  Olalekan J. Akintande³  Olusanya E. Olubusoye⁴  Adeola F. Adenikinju⁴ 

[1] Centre for Econometric and Allied Research, University of Ibadan, Ibadan, Nigeria &Centre for Petroleum, Energy Economics and Law, University of Ibadan, Ibadan, Nigeria;;Computational Statistics Unit, Department of Statistics, University of Ibadan, Nigeria, &;Laboratory for Interdisciplinary Statistical Analysis, Department of Statistics, University of Ibadan, Ibadan, Nigeria &

关键词: Coronavirus pandemic;    Energy market;    Machine learning;    Uncertainty;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown