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 …

We examine the performance of volatility models that incorporate features such as long (short) memory, regime …-t). Second, we perform a comprehensive panel forecasting analysis of the MSM models as well as other competing volatility models … over the alternative volatility models in terms of mean absolute forecast errors and that (iii) forecast combinations …

This paper examines time-varying stock price and volatility dynamics of constituent industry sector indices in the … of return during rises in aggregate stock market volatility. Finally, this paper identifies which industries exhibit the … highest degree of volatility persistence and how this impacts their respective beta estimates. It shows time-dependence in …

Ramadan, the holy month for the Muslims, with the market return, volatility and trade volume in the of DSE. Applying GJR … stock market return and volatility. However, Ramadan has a significant negative impact on the daily trade volume of DSE …

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. …

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 …

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 last decade has seen substantial advances in the measurement, modeling and forecasting of volatility which has … centered around the realized volatility literature. To date, most of the focus has been on the daily and monthly frequency …, with little attention on longer horizons such as the quarterly frequency. In finance applications, forecasts of volatility …

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

Volatility has been used as an indirect means for predicting risk accompanied with an asset. Volatility explains the … variations in returns. Forecasting volatility has been a stimulating problem in the financial systems. This study examined the … different volatility estimators and determined the most efficient volatility estimator. The study described the accuracy of the …