This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new …

forecasts of daily integrated volatility. Our approach is based on a two-step shrinkage procedure designed to extract latent … common volatility factors from a large dimensional and high-frequency asset returns dataset. In the first step, we apply …, in order to estimate a latent return factor. This new factor is in turn utilized to construct a latent volatility factor …

In this paper, we study the methods of combining different volatility forecasts using various GARCH models. Given that … the major risk exposure for many investors in energy is the volatility of the electricity price, our motivation stems from … the fact that there is no single best model for forecasting such volatility. Ample evidence suggests that most of the …

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 …

The contributions of error distributions have been ignored while modeling stock market volatility in Nigeria and … studies have shown that the application of appropriate error distribution in volatility model enhances efficiency of the model … asymmetric volatility models each in Normal, Student's-t and generalized error distributions with the view to selecting the best …

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management … the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns.In turn, this so …-called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure …

high-frequency data better and produce more accurate forecasts than competing realized volatility and option …

We compare more than 1000 different volatility models in terms of their fit to the historical ISE-100 Index data and … for modeling the ISE-100 return volatility. The t-distribution seems to characterize the distribution of the heavy tailed … model to the historical ISE-100 return data indicates that the return volatility reacts to bad news 24% more than they react …