algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information …. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer …

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 …