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Shortages and Machine-Learning Forecasting of Oil Returns Volatility: 1900-2024

Author

Listed:
  • Onur Polat

    (Department of Public Finance, Bilecik Seyh Edebali University, Bilecik, Turkiye)

  • Dhanashree Somani

    (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA)

  • Rangan Gupta

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

  • Sayar Karmakar

    (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA)

Abstract

The objective of this paper is to forecast the volatility of the West Texas Intermediate (WTI) oil returns over the monthly period of January 1900 to June 2024 by utilizing the information content of newspapers articles-based indexes shortages for the United States (US). We measure volatility as the inter-quantile range by fitting a Bayesian time-varying parameter quantile regression (TVP-QR) on oil returns. The TVP-QR is also used to estimate skewness, kurtosis, lower- and upper-tail risks, and we control for them in our forecasting model along with leverage. Based on the Lasso estimator to control for overparameterization, we find that the model with moments outperform the benchmark autoregressive model involving 12 lags of volatility. More importantly, the performance of the moments-based model improves further when we incorporate the aggregate metric of shortages and its sub-indexes, particularly those related to the industry and labor sectors. These findings carry significant implications for investors.

Suggested Citation

  • Onur Polat & Dhanashree Somani & Rangan Gupta & Sayar Karmakar, 2025. "Shortages and Machine-Learning Forecasting of Oil Returns Volatility: 1900-2024," Working Papers 202503, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202503
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    More about this item

    Keywords

    Oil market volatility; Shortages; Bayesian Time-Varying Parameter Quantile Regressions; Lasso Estimator; Forecasting;
    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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