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

Afees Salisu, Ahamuefula Ogbonna and Rangan Gupta ()
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Ahamuefula Ogbonna: Centre for Econometrics & Applied Research, Ibadan, Nigeria
Rangan Gupta: Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa

No 202542, Working Papers from University of Pretoria, Department of Economics

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.

Keywords: Oil price volatility; Global economic conditions; Climate concerns; Forecasting (search for similar items in EconPapers)
JEL-codes: C32 C53 Q41 Q47 Q54 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2025-11
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202542

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