In this paper , we propose a forecasting model for volatility based on its decomposition to several investment horizons and jumps . As a forecasting tool, we use Realized GARCH framework which models jointly returns and realized measures of volatility. Using jump wavelet two scale realized volatility estimator (JWTSRV) , we first decompose the returns volatility into several investment horizons and jumps and then utilise this decomposition in a newly proposed Realized Jump-GARCH and Realized Wavelet-Jump GARCH models. On currency futures data covering the period of recent financial crisis  we moreover compare the forecasts from Realized GARCH model using several additional realized volatility measures. Namely, we use the realized volatility, bipower variation, two-scale realized volatility, realized kernel and jump wavelet two scale realized volatility. 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 .
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 .
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This paper investigates how the forecasts of volatility vary with different high frequency measures. In addition, using
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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
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. As a forecasting tool, we use Realized GARCH framework which models jointly returns and realized measures of volatility. Using
is possible due to a recently proposed
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, we first decompose the returns volatility into several investment horizons and jumps and then utilise this decomposition in a newly proposed Realized Jump-GARCH and Realized Wavelet-Jump GARCH models. On currency
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the forecasts from Realized GARCH model
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Namely, we use the realized volatility, bipower variation, two-scale realized volatility, realized kernel and jump wavelet two scale realized volatility. We find that in-sample as well as out-of-sample performance
Our results show that inclusion of jumps and realized measures robust to noise improves forecasting ability
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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
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