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An Information-Based Index of Uncertainty and the predictability of Energy Prices

Author

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  • Olubusoye, Olusanya E
  • Yaya, OlaOluwa S.
  • Ogbonna, Ahamuefula

Abstract

We develop an index of uncertainty, the COVID-19 induced uncertainty (CIU) index, and employ it to empirically examine the vulnerability of energy prices amidst the COVID-19 pandemic using a distributed lag model that jointly accounts for conditional heteroscedasticity, autocorrelation, persistence, and structural breaks, as well as day-of-the-week effect. The nexus between energy returns and uncertainty index is analyzed, using daily price returns of eight energy sources (Brent oil, diesel, gasoline, heating oil, kerosene, natural gas, propane, and WTI oil) and four news/information-based uncertainty proxies [CIU, EPU, Global Fear Index (GFI) and VIX]. The CIU and alternative indexes are used, respectively for the main estimation and sensitivity analysis. We show the outperformance of CIU over alternative news uncertainty proxies in the prediction of energy prices. News (aggregate) and bad news are found to negatively and significantly impact energy returns, while good news has a significantly positive impact. Imperatively, energy variables lack hedging potentials against the uncertainty occasioned by the COVID-19 pandemic, while we find no strong evidence of asymmetry. Our results are robust to the choice of news variables, forecast horizons employed, with likely sensitivity to energy prices.

Suggested Citation

  • Olubusoye, Olusanya E & Yaya, OlaOluwa S. & Ogbonna, Ahamuefula, 2021. "An Information-Based Index of Uncertainty and the predictability of Energy Prices," MPRA Paper 109839, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:109839
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    References listed on IDEAS

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    Cited by:

    1. Indre Siksnelyte-Butkiene, 2021. "Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
    2. Afees A. Salisu & Ahamuefula E. Ogbonna & Tirimisiyu F. Oloko & Idris A. Adediran, 2021. "A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    3. Olubusoye, Olusanya E & Akintande, Olalekan J. & Yaya, OlaOluwa S. & Ogbonna, Ahamuefula & Adenikinju, Adeola F., 2021. "Energy Pricing during the COVID-19 Pandemic: Predictive Information-Based Uncertainty Indexes with Machine Learning Algorithm," MPRA Paper 109838, University Library of Munich, Germany.
    4. Li, Zepei & Huang, Haizhen, 2023. "Challenges for volatility forecasts of US fossil energy spot markets during the COVID-19 crisis," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 31-45.

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    More about this item

    Keywords

    Distributed lag Model; Energy; Google Trends; Hedging Potential; Uncertainty;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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