Before revision:  We find that in-sample as well as out-of-sample performance of the model significantly differs based on the realized measure used. When JWTSRV estimator is used, model produces significantly best forecasts. Our Realized Wavelet-Jump GARCH model proves to further improve the volatility forecasts. We conclude that realized volatility measurement in the time-frequency domain and inclusion of jumps improves the volatility forecasting considerably .
After revision:  We find that in-sample as well as out-of-sample performance of the model considerably. Thus for a forecaster, it is crucial to use proper high frequency measure. An interesting insight into the volatility process is also provided by its decomposition. We find that most of the information for future volatility comes from high frequency part of the spectra representing very short investment horizons .