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Aggregate versus Disaggregated Geopolitical Risks and Machine Learning Forecasting of Oil Market Volatility: Evidence from AI-Based Indexes

Author

Listed:
  • Onur Polat

    (Hacettepe University, Informatics Institute 06800 Beytepe, Ankara, Türkiye)

  • Goodness C. Aye

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa; Department of Economics, Landmark University, Omu-Aran, Kwara State, Nigeria)

  • Rangan Gupta

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

  • Samrat Goswami

    (Department of Rural Studies, Tripura University, Agartala 799022, Tripura, India)

Abstract

This study evaluates the out-of-sample predictive power of disaggregated versus aggregate geopolitical risks (GPR) for crude oil market volatility. Leveraging the AI-GPR dataset of Iacoviello and Tong (2026), we forecast monthly WTI realized volatility over the 1960:01--2026:09 period across 1- to 24- month horizons using autoregressive and machine learning benchmarks. While the predictive content of the aggregate GPR index is largely localized to the 1990s, oil-specific and event-type risk components provide persistent incremental information through the post-2000 period. When allowed to weight the eight underlying event types endogenously, the model significantly outperforms the headline index, reducing root mean squared forecast errors by 2.1 percent to 7.5 percent at horizons up to 12 months. Conversely, geographic disaggregations (country-level and bilateral) yield negligible forecasting value. In addition, superior performance from random forests reflects intrinsic non-linear volatility dynamics rather than geopolitical risk extraction.

Suggested Citation

  • Onur Polat & Goodness C. Aye & Rangan Gupta & Samrat Goswami, 2026. "Aggregate versus Disaggregated Geopolitical Risks and Machine Learning Forecasting of Oil Market Volatility: Evidence from AI-Based Indexes," Working Papers 202636, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202636
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F51 - International Economics - - International Relations, National Security, and International Political Economy - - - International Conflicts; Negotiations; Sanctions
    • 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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