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

Author

Listed:
  • 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

    as
    1. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).
    2. OlaOluwa S. Yaya & Ahamuefula E. Ogbonna & Robert Mudida & Nuruddeen Abu, 2021. "Market efficiency and volatility persistence of cryptocurrency during pre‐ and post‐crash periods of Bitcoin: Evidence based on fractional integration," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1318-1335, January.
    3. Joakim Westerlund & Paresh Narayan, 2015. "Testing for Predictability in Conditionally Heteroskedastic Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 342-375.
    4. Bannigidadmath, Deepa & Narayan, Paresh Kumar, 2016. "Stock return predictability and determinants of predictability and profits," Emerging Markets Review, Elsevier, vol. 26(C), pages 153-173.
    5. Westerlund, Joakim & Narayan, Paresh Kumar, 2012. "Does the choice of estimator matter when forecasting returns?," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2632-2640.
    6. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    7. Yaya, OlaOluwa S. & Ogbonna, Ahamuefula E. & Olubusoye, Olusanya E., 2019. "How persistent and dynamic inter-dependent are pricing of Bitcoin to other cryptocurrencies before and after 2017/18 crash?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    8. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    9. Yaya, OlaOluwa S & Ogbonna, Ephraim A, 2019. "Do we Experience Day-of-the-week Effects in Returns and Volatility of Cryptocurrency?," MPRA Paper 91429, University Library of Munich, Germany.
    10. Narayan, Paresh Kumar & Gupta, Rangan, 2015. "Has oil price predicted stock returns for over a century?," Energy Economics, Elsevier, vol. 48(C), pages 18-23.
    11. Donia Aloui & Stéphane Goutte & Khaled Guesmi & Rafla Hchaichi, 2020. "COVID 19's impact on crude oil and natural gas S&P GS Indexes," Working Papers halshs-02613280, HAL.
    12. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2018. "Is stock return predictability time-varying?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 152-172.
    13. Corinne Le Quéré & Robert B. Jackson & Matthew W. Jones & Adam J. P. Smith & Sam Abernethy & Robbie M. Andrew & Anthony J. De-Gol & David R. Willis & Yuli Shan & Josep G. Canadell & Pierre Friedlingst, 2020. "Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement," Nature Climate Change, Nature, vol. 10(7), pages 647-653, July.
    14. Salisu, Afees A. & Oloko, Tirimisiyu F., 2015. "Modeling oil price–US stock nexus: A VARMA–BEKK–AGARCH approach," Energy Economics, Elsevier, vol. 50(C), pages 1-12.
    15. Wang, Jian & Shao, Wei & Kim, Junseok, 2020. "Analysis of the impact of COVID-19 on the correlations between crude oil and agricultural futures," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    16. Dang, Hai-Anh H. & Trinh, Trong-Anh, 2021. "Does the COVID-19 lockdown improve global air quality? New cross-national evidence on its unintended consequences," Journal of Environmental Economics and Management, Elsevier, vol. 105(C).
    17. Akintande, Olalekan J. & Olubusoye, Olusanya E. & Adenikinju, Adeola F. & Olanrewaju, Busayo T., 2020. "Modeling the determinants of renewable energy consumption: Evidence from the five most populous nations in Africa," Energy, Elsevier, vol. 206(C).
    18. Zhang, Jilin & Lai, Yongzeng & Lin, Jianghong, 2017. "The day-of-the-Week effects of stock markets in different countries," Finance Research Letters, Elsevier, vol. 20(C), pages 47-62.
    19. Salisu, Afees A. & Ogbonna, Ahamuefula E., 2019. "Another look at the energy-growth nexus: New insights from MIDAS regressions," Energy, Elsevier, vol. 174(C), pages 69-84.
    20. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    21. Conlon, Thomas & McGee, Richard, 2020. "Safe haven or risky hazard? Bitcoin during the Covid-19 bear market," Finance Research Letters, Elsevier, vol. 35(C).
    22. Salisu, Afees A. & Swaray, Raymond & Oloko, Tirimisiyu F., 2019. "Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables," Economic Modelling, Elsevier, vol. 76(C), pages 153-171.
    23. Afees A. Salisu & Ahamuefula E. Ogbonna & Idris Adediran, 2021. "Stock‐induced Google trends and the predictability of sectoral stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 327-345, March.
    24. Corbet, Shaen & Larkin, Charles & Lucey, Brian, 2020. "The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies," Finance Research Letters, Elsevier, vol. 35(C).
    25. Narayan, Paresh Kumar & Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Westerlund, Joakim, 2016. "Are Islamic stock returns predictable? A global perspective," Pacific-Basin Finance Journal, Elsevier, vol. 40(PA), pages 210-223.
    26. Sharif, Arshian & Aloui, Chaker & Yarovaya, Larisa, 2020. "COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach," International Review of Financial Analysis, Elsevier, vol. 70(C).
    27. Esfahani, Hadi Salehi & Ramirez, Maria Teresa, 2003. "Institutions, infrastructure, and economic growth," Journal of Development Economics, Elsevier, vol. 70(2), pages 443-477, April.
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    Citations

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    Cited by:

    1. Tirimisiyu F. Oloko & Ahamuefula E. Ogbonna & Idris A. Adediran, 2024. "Digital Currencies and Macroeconomic Performance: A Global Perspective," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 27(2), pages 351-394, May.
    2. 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.
    3. 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.
    4. 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.
    5. Khurshid, Adnan & Khan, Khalid & Cifuentes-Faura, Javier & Chen, Yufeng, 2024. "Asymmetric multifractality: Comparative efficiency analysis of global technological and renewable energy prices using MFDFA and A-MFDFA approaches," Energy, Elsevier, vol. 289(C).
    6. 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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