We augment the HAR model with additional information channels to forecast realized volatility of WTI futures prices. These channels include stock markets, sentiment indices, commodity and FX markets, and text-based Google indices. We then apply four differing machine learning techniques to...

This study introduces a novel approach to predicting global oil demand by integrating machine learning (ML) techniques to forecast consumption across seven refined oil products and seven key regions. By aggregating these forecasts, we offer a comprehensive view of global demand trends. The paper...