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Forecasting Oil Volatility Using a GARCH-MIDAS Approach: The Role of Global Economic Conditions

Author

Listed:
  • Afees A. Salisu

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam; Faculty of Business Administration, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Elie Bouri

    (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon)

  • Qiang Ji

    (Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

In this study, we offer two main innovations. First, we subject six alternative indicators of global economic activity, including the one recently developed by Baumeister et al. (2020), to empirical tests of their relative predictive powers for crude oil market volatility. Second, we accommodate all the relevant series at their available data frequencies using the GARCH-MIDAS approach, thereby circumventing information loss and any associated bias. We find evidence in support of the ability of global economic activity to predict energy market volatility. Our forecast evaluation of the various indicators places a higher weight on the newly developed indicator of global economic activity by Baumeister et al. (2020), based on a set of 16 variables covering multiple dimensions of the global economy, than other indicators. The results leading to these conclusions are robust to multiple forecast horizons and consistent across alternative energy sources.

Suggested Citation

  • Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020. "Forecasting Oil Volatility Using a GARCH-MIDAS Approach: The Role of Global Economic Conditions," Working Papers 202051, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202051
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    References listed on IDEAS

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    Cited by:

    1. Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022. "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, vol. 108(C).
    2. Riza Demirer & Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2020. "Effect of Rare Disaster Risks on Crude Oil: Evidence from El Nino from Over 140 Years of Data," Working Papers 2020104, University of Pretoria, Department of Economics.
    3. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    4. Afees A. Salisu & Philip C. Omoke & Abdulsalam Abidemi Sikiru, 2023. "Geopolitical risk and global financial cycle: Some forecasting experiments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 3-16, January.
    5. Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers," Energies, MDPI, vol. 14(14), pages 1-15, July.

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    More about this item

    Keywords

    Energy Markets Volatility; Global Economic Conditions; Mixed-Frequency;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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