variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011 …

We propose an automatic machine-learning system to forecast realized volatility for S&P 100 stocks using 118 features … extraordinary performance across forecast horizons, and the improvement in out-of-sample R2's translates into nontrivial economic …

We augment the HAR model with additional information channels to forecast realized volatility of WTI futures prices … baseline model forecasts. We show that machine learning generated forecasts provide better forecasting quality and that … forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to …

combination models. The results highlight the efficiency of machine learning models for oil futures volatility forecasting, in …This paper comprehensively investigates the connection between oil futures volatility and the financial market based on … particular ensemble models and neural network models. Most interestingly, we are the first to consider the “forecast combination …