This paper investigates how the forecasts of volatility vary with different high frequency measures. In addition, using a forecasting model based on Realized GARCH combined with time-frequency decomposed volatility, we attempt to study the influence of intra-day investment horizons on daily volatility forecasts. The decomposition of volatility into several investment horizons and jumps is possible due to a recently proposed jump wavelet two scale realized volatility estimator (JWTSRV). On exchange rate futures data covering the recent financial crisis , we moreover compare forecasts using several additional realized volatility measures . 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 . 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  .
This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of different timescales on volatility forecasts. The decomposition of volatility into several timescales approximates the behaviour of traders at corresponding investment horizons. The proposed methodology is moreover able to account for impact of jumps due to a recently proposed jump wavelet two scale realized volatility estimator . We propose a realized Jump-GARCH models estimated in two versions using maximum likelihood as well as observation-driven estimation framework of generalized autoregressive score. We compare forecasts using several popular realized volatility measures on foreign exchange rate futures data covering the recent financial crisis . Our results indicate that disentangling jump variation from the integrated variation is important for forecasting performance . An interesting insight into the volatility process is also provided by its multiscale decomposition. We find that most of the information for future volatility comes from high frequency part of the spectra representing very short investment horizons . Our newly proposed models outperform statistically the popular as well conventional models in both one-day and multi-period-ahead forecasting .
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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
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