We evaluate the performance of several linear and nonlinear machine learning (ML) models in forecasting the realized … volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a … can statistically outperform linear models; and (iii) all ML models show a tendency to underestimate the RV in high-volatility …

forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting … and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic … volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically …

forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting … and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic … volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically …

This paper proposes a new method of forecasting realized volatilities by exploiting their common dynamics within a … estimates. We apply the new models to vectors of up to 30 daily realized volatility series of stocks composing the Dow Jones …

We evaluate the performance of several linear and nonlinear machine learning models in forecasting the realized … volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a …

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 …