financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms …

topological stock market changes as well as the incorporation of these topological changes into forecasting realized volatility …

returns plays an important role for volatility forecasting. Additionally, models utilizing a logarithmic transformation of the … an easy-to-use and accurate tool for realized variance forecasting, whose performance may potentially be further improved …

successfully used as a nonparametric tool for regression estimation and forecasting time series data. In this thesis, we deal with … the application of SVRs in financial markets volatility forecasting.An accurate forecast of volatility is essential to …

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 …