High frequency stock return data tend to exhibit characteristics such as volatility clustering, volatility persistence, leverage effects, and properties of nonnormal unconditional distributions reflected in the form of skewness, high peakedness, and excess kurtosis. Although traditional GARCH models that employ leptokurtic distributions have been found useful to account for the conditional heteroscedasticity and leptokurtosis, they have difficulty in accommodating other stylized effects commonly observed in high frequency data. This paper attempts to rectify this deficiency by introducing a more general GJR IGARCH-EGB2 model, which not only considers the flexible distributional characteristics associated with the exponential beta distribution, but also incorporates the asymmetric conditional variance and integrated GARCH process into model consideration. Likelihood ratio tests, goodness of fit tests, distribution plots, and out-of-sample forecasts generate a preponderance of evidence to support the innovative GJR IGARCH-EGB2 specification over conventional competing alternatives presented in the literature.
