Before revision:  Our results show that inclusion of jumps and realized measures robust to noise improves forecasting ability of the model considerably. Thus for a forecaster, it is crucial to use proper high frequency measure .
After revision:  Our results indicate that disentangling jump variation from the integrated variation is important for forecasting performance .