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Forecasting Oil Price Volatility of the United States: The Role of State-Level Climate Concern Indexes

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)

Abstract

This study explores how variations in climate-related concerns/sentiments influence oil market uncertainty, utilizing the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Mixed Data Sampling (MIDAS) model to forecast the daily volatility of West Texas Intermediate (WTI) crude oil returns in the United States. Drawing on monthly measures of both aggregate and state-level Climate Concern Indexes (CCIs) from January 2004 to July 2025, the analysis indicates that stronger expressions of climate concern, across different regions, tend to heighten volatility in oil prices. When the GARCH-MIDAS model incorporates these climate indicators, and when several forecast combination techniques are applied to the state-level CCIs, predictive accuracy improves substantially relative to the baseline GARCH-MIDAS specification using the Global Economic Conditions Index (GARCH-MIDAS-GECON). Among the alternative specifications, the least-squares weighting combination delivers the most consistent gains, both statistically and in terms of economic utility (gains). Overall, the findings demonstrate that acknowledging the regional diversity in climate attitudes provides valuable forecasting advantages for oil price dynamics, offering insights relevant to both investors and policymakers.

Suggested Citation

  • Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta, 2025. "Forecasting Oil Price Volatility of the United States: The Role of State-Level Climate Concern Indexes," Working Papers 202542, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202542
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    Keywords

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    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
    • 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
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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