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Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning

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  • Luo, Jiawen
  • Klein, Tony
  • Walther, Thomas
  • Ji, Qiang

Abstract

Extending the popular HAR model with additional information channels to forecast realized volatility of WTI futures prices, we show that machine learning generated forecasts provide better forecasting quality and that portfolios which are constructed with these forecasts outperform their competing models and resulting in economic gains. Analyzing the selection process, we show that information channels vary across forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to the significance of information channels.

Suggested Citation

  • Luo, Jiawen & Klein, Tony & Walther, Thomas & Ji, Qiang, 2021. "Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning," QBS Working Paper Series 2021/04, Queen's University Belfast, Queen's Business School.
    • Handle: RePEc:zbw:qmsrps:202104
    DOI: 10.2139/ssrn.3701000
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    More about this item

    Keywords

    Forecasting; Crude oil; Realized volatility; Exogenous predictors; Machine learning;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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