Realized Wavelet Jump-GARCH model: Can time-frequency decomposition of volatility improve its forecasting?
AbstractIn 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.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1204.1452.
Date of creation: Apr 2012
Date of revision: Feb 2013
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-04-17 (All new papers)
- NEP-ECM-2012-04-17 (Econometrics)
- NEP-ETS-2012-04-17 (Econometric Time Series)
- NEP-FOR-2012-04-17 (Forecasting)
- NEP-MST-2012-04-17 (Market Microstructure)
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