vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is … giving different weights to each model. In particular, four volatility models are used in RV forecasting as basic models. For …

In this paper, we estimate, model and forecast Realized Range Volatility, a new realized measure and estimator of the …-known stylized effects present in financial data. We consider an HAR model with asymmetric effects with respect to the volatility and … distribution for the innovations. The analysis of the forecast performance during the different periods suggests that including the …

forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting … volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically … ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility …

forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting … volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically … ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility …

realized volatility (RV). This paper also proposes a novel neural network model for multi-step forecasting that systematically …This paper proposes a novel algorithm called Persistent Homology for Realized Volatility (PHRV), which aims to … effectively incorporate persistent homology (PH) into neural network models to increase their forecast accuracy in predicting …

realized volatility (RV). This paper also proposes a novel neural network model for multi-step forecasting that systematically …This paper proposes a novel algorithm called Persistent Homology for Realized Volatility (PH-RV), which aims to … effectively incorporate persistent homology (PH) into neural network models to increase their forecast accuracy in predicting …