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 …

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 …

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 horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve …

This paper studies predictability of realized volatility of U.S. Treasury futures using high-frequency data for 2-year … to generate systemic under-predictions of future realized volatility. …

volatility, but without the estimation problems associated with the latter, and being applicable in the multivariate setting for … model in several ways, it allows for all the primary stylized facts of financial asset returns, including volatility … EM-algorithm is developed for estimation. Each element of the vector return at time t is endowed with a common univariate …

Rogers-Satchell (RS) measure is an efficient volatility measure. This paper proposes quantile RS (QRS) measure to … on Standard and Poor 500 and Dow Jones Industrial Average indices show that volatility estimates using QRS measures …-of-sample forecast. For return models, the constant mean structure with Student-t errors and QRS volatility estimates provides the best …

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 …

There is evidence that volatility forecasting models that use intraday data provide better forecast accuracy as … fills this gap in the literature and extends previous studies on forecasting stock market volatility in several important …, we use forecast horizons ranging from 1 day to 6 months. Third, we evaluate the precision of volatility forecast provided …