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 introduces the Markov-Switching Multifractal Duration (MSMD) model by adapting the MSM stochastic volatility … feature of durations generated by the MSMD process propagates to counts and realized volatility. We employ a quasi …

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 …

This study investigates the performance of five models for forecasting the Egyptian stock market return volatility. We … examined also the ARCH effect to test the validity of using GARCH family to predict the volatility of market indices. The … between errors of volatility forecasting models, we found no significance difference between the errors of competing models …

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 …

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many … forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from … these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …