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 …

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 …

This paper attempts to explore the comparative ability of different statistical and econometric volatility forecasting … models in the context of Zimbabwe stock market. Two different models were considered in this study. The volatility of the ZSE … volatility clustering, fat tails and leverage effect, these models are GARCH (1,1) and exponential GARCH (1,1). The first model …

This study examines the statistical properties required to model the dynamics of both the returns and volatility series … adequately estimate long-memory dynamics in returns and volatility. The in-sample diagnostic tests as well as out … conditional volatility and strongly support the estimation of dynamic returns that allow for time-varying correlations. A …

In this paper, we used the GARCH (1,1) and GARCH-M (1,1) models to investigate volatility and persistence at daily … persistence of volatility, meaning that the conditional volatility tends to revert faster to the long-term mean than the other … statistically significant and positive (thus confirming the hypothesis that an increase in volatility leads an increase in future …

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 …

-GARCH models to capture time varying volatility and nonlinearity in petrol prices. ANN augmented versions of LSTAR-LST-GARCH models … models, except for the MLP-FIGARCH and MLP-FIAPGARCH models. iv. Volatility clustering, asymmetry and nonlinearity …

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 …

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