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...

We examine the predictive value of expected skewness of oil returns for the realized volatility using monthly data from 1859:11 to 2023:04. We utilize a quantile predictive regression model, which is able to accommodate nonlinearity and structural breaks. In-sample results show that the...

We examine the impact of the global economic activity, oil supply, oil-specific consumption demand, and oil inventory demand shocks on the expected aggregate skewness of the United States (US) economy, obtained based on a data-rich environment involving 211 macroeconomic and financial variables...