Forecasting realized volatility of crude oil futures prices based on machine learning

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 that are constructed with these forecasts outperform their competing models 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.