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 …

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 contributions of error distributions have been ignored while modeling stock market volatility in Nigeria and … studies have shown that the application of appropriate error distribution in volatility model enhances efficiency of the model … asymmetric volatility models each in Normal, Student's-t and generalized error distributions with the view to selecting the best …

In this paper, we study the methods of combining different volatility forecasts using various GARCH models. Given that … the major risk exposure for many investors in energy is the volatility of the electricity price, our motivation stems from … the fact that there is no single best model for forecasting such volatility. Ample evidence suggests that most of the …

We propose a model that extends the RT-GARCH model by allowing conditional heteroskedasticity in the volatility process …. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple … setting. The volatility forecast function follows a second-order difference equation as opposed to first-order under GARCH(1 …

form the best linear forecasts for future volatility we find that the behavioral model generates sensible forecasts that …

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 …

. This study uses the Diebold and Yilmaz index model to analyze and measure volatility spillovers and interconnectedness … among APEC stock markets. The objective is to identify major transmitters of volatility spillovers and assess the magnitude … of different crisis cycles. The results show that the US is the major contributor (69.54%) to volatility spillovers in …

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 …

characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in … market volatility, with significant impact on pricing and forecasting of market volatility. The implication is that models … that accomodate long memory hold the promise of improved long-run volatility forecast as well as accurate pricing of long …