model in several ways, it allows for all the primary stylized facts of financial asset returns, including volatility … volatility, but without the estimation problems associated with the latter, and being applicable in the multivariate setting for …

prices. We empirically assess efficiency gains in volatility estimation when using range-based estimators as opposed to … simple daily ranges and explore the use of these more efficient volatility measures as predictors of daily ranges. The array … forecasts are produced by a realized range based HAR model with a GARCH volatility-of-volatility component. …

This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous … autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump …

such as the realized volatility and squared overnight returns, are confronted with those from ARFIMA realized volatility …

improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source … for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … conjunction with a variety of volatility models for returns on the Standard & Poor's 100 stock index. We consider two so …

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 …

Extracting and forecasting the volatility of financial markets is an important empirical problem. Time series of … realized volatility or other volatility proxies, such as squared returns, display long range dependence. Exponential smoothing … address the prediction of volatility by a FerIMA model and carry out a recursive forecasting experiment, which proves that the …

We propose a new class of conditional heteroskedasticity in the volatility (CH-V) models which allows for time …-varying volatility of volatility in the volatility of asset returns. This class nests a variety of GARCH-type models and the SHARV model … of Ding (2021b). CH-V models can be seen as a special case of the stochastic volatility of volatility model. We then …

Nowadays, modeling and forecasting the volatility of stock markets have become central to the practice of risk … to forecast the volatility of the Moroccan stock-market index MADEX. We use daily returns covering the period between 01 …, as well as leading to a better understanding of the Moroccan stock-exchange volatility dynamics, especially with the lack …

This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset … predictive performance relative to the standard volatility models. Furthermore, we construct volatility timing portfolios and …