, forecasting and pricing “good” and “bad” volatilities based on realized variation type measures constructed from high … most noteworthy empirical findings to date pertaining to volatility forecasting and asset pricing …

covers different types of forecasting applications encountered in the literature. We are concerned with 1-step …

forecasting performance on days with volatility jumps for 23 NASDAQ stocks from 27 July 2007 to 18 November 2016. A simple … best forecasting performance for both normal and jump volatility days. Finally, we use Integrated Gradients and SHAP …

This paper examines, for the first time, the performance of machine learning models in realised volatility forecasting … sample period of July 27, 2007, to November 18, 2016. We find strong evidence to support ML forecasting power dominating an …-ML has very strong forecasting power and adding news sentiment variables to the data set only improves the forecasting power …

describe the implementation of the BAC estimator in case of microstructure noise and jumps. We further present more detailed …