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 …

The volatility of equity and foreign exchange market is an important input to portfolio selection and to asset pricing … models. Many investment decisions and valuation of derivatives frequently rely on predictions of volatility. In this paper we … review the existing empirical literature in forecasting volatility of financial time series. Particularly, we decompose the …

We propose a methodology to include night volatility estimates in the day volatility modeling problem with high … the natural relationship between the realized measure and the conditional variance. This improves volatility modeling by … leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate models that …

volatility índices (namely the originally created RTSVX and the new RVI that has replaced it), using daily data over the period …

improved volatility measurements but has also inspired research into their potential value as an information source for … volatility forecasting. In this paper we explore the forecasting value of historical volatility (extracted from daily return … series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of …

Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments … implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors …, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility …

In this paper, we estimate, model and forecast Realized Range Volatility, a realized measure and estimator of the … forecasting daily stocks volatility. We consider an HAR model with asymmetric effects with respect to the volatility and the … volatility series and they have a highly in-sample explanatory power. The analysis of the forecast performance in 16 NYSE stocks …

appropriate volatility measure for most financial applications. For a wide variety of markets, the GARCH, EGARCH, and GJR (or … Guan (2005, JFM) does not. Furthermore, the GARCH and GJR forecasts are especially biased following high volatility days … which cause a large jump in forecast volatility which is rarely fully realized …

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 …