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Supply Disruptions and Predictability of Oil Returns Volatility: A GARCH-MIDAS Approach

Author

Listed:
  • Afees A. Salisu

    (Centre for Econometrics & Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Ahamuefula E. Ogbonna

    (Centre for Econometrics & Applied Research, Ibadan, Nigeria)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Luis A. Gil-Alana

    (Faculty of Economics and ICS, University of Navarra, E-31080 Pamplona, Spain; Universidad Francisco de Vitoria, Facultad de Ciencias Juridicas y Empresariales, Madrid, Spain)

Abstract

We utilize a Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) framework to show that monthly indicators of global supply concerns and the United States, as well as its regional and product-specific indexes, based on 200 million shipment-level transactions, produces an increase in daily West Texas Intermediate (WTI) oil returns volatility over the period of January 2013 to August 2024. Furthermore, in-sample predictability for oil returns volatility carries over to statistically significant and robust short-, medium, and long-term forecasting gains derived from the information content of the various supply disruption indexes, relative to a benchmark model of GARCH-MIDAS-realized volatility (RV), over the out-of-sample period of January 2020 and beyond. The elaborate indexes of considered supply disruption is also shown to outperform other popular monthly measures defining global supply restrictions and economic conditions, when incorporated in the GARCH-MIDAS model. Our results have important implications for investors and policymakers.

Suggested Citation

  • Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta & Luis A. Gil-Alana, 2025. "Supply Disruptions and Predictability of Oil Returns Volatility: A GARCH-MIDAS Approach," Working Papers 202502, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202502
    as

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    More about this item

    Keywords

    Oil markets volatility; Supply disruptions; GARCH-MIDAS model; Predictability;
    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
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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