The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this … paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility …, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model …

frequency volatilities and correlations ; Dynamic conditional correlation ; Spline-GARCH ; Idiosyncratic volatility ; Long …

while adjusting for the volatility risk premium. Relative model performance does not change during the global financial …

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 …

In this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our … approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors …, we apply either LASSO or elastic net shrinkage on estimates of integrated volatility of all constituents in the dataset …

We propose a model that extends the RT-GARCH model by allowing conditional heteroskedasticity in the volatility process …. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple … setting. The volatility forecast function follows a second-order difference equation as opposed to first-order under GARCH(1 …

This study examines the statistical properties required to model the dynamics of both the returns and volatility series … adequately estimate long-memory dynamics in returns and volatility. The in-sample diagnostic tests as well as out … conditional volatility and strongly support the estimation of dynamic returns that allow for time-varying correlations. A …

issue is particularly important for persistent time series, we focus on volatility modelling, specifically modelling of … realized volatility. We suggest a simple way of adjusting volatility models, which we illustrate on an AR(1) model and the HAR … more than 15 years, and we find that our extension improves the volatility models—both in sample and out of sample. For HAR …