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Modeling and Forecasting Crude Oil Price Volatility: Evidence from Historical and Recent Data

Author

Listed:
  • Thomas Lux

    (Department of Economics, University of Kiel, Kiel, Germany)

  • Mawuli K. Segnon

    (Department of Economics, University of Kiel, Germany)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

Abstract

This paper uses the Markov-switching multifractal (MSM) model and generalized autoregressive conditional heteroscedasticity (GARCH)-type models to forecast oil price volatility over the time periods from January 02, 1875 to December 31, 1895 and from January 03, 1977 to March 24, 2014. Based on six di erent loss functions and by means of the superior predictive ability (SPA) test, we evaluate and compare their forecasting performance at short and long horizons. The empirical results indicate that none of our volatility models can uniformly outperform other models across all six different loss functions. However, the new MSM model comes out as the model that most often across forecasting horizons and subsamples cannot be outperformed by other models, with long memory GARCH-type models coming out second best.

Suggested Citation

  • Thomas Lux & Mawuli K. Segnon & Rangan Gupta, 2015. "Modeling and Forecasting Crude Oil Price Volatility: Evidence from Historical and Recent Data," Working Papers 201511, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201511
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    Cited by:

    1. Mawuli Segnon & Thomas Lux & Rangan Gupta, 2015. "Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-Type Volatility Models," Working Papers 201550, University of Pretoria, Department of Economics.

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    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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