Forecasting of return Fluctuations using by combining discrete wavelet transform and GARCH model
This research is an attempt to introduce a desirable model for modeling and forecasting the fluctuations of financial processes. For modelling the fluctuations of financial processes, we have used the combination of the GARCH model and the discrete wavelet transform. In this thesis, we are presented a model for forecasting fluctuations of returns of exchange price index. Stock price index data has been reviewed. The data was collected from the site from 1/1/1390 to 29/12/1396. Due to the importance of return on financial data, the returns series is calculated and applied for modeling. After preparing data, the two combination models namely ARMA-ARCH and DWT-GARCH are fitted to the data series. The results show that the DWT-GARCH model has better performance than the ARMA-ARCH model. The DWT-GARCH model can significantly improve prediction outcomes and reduce the conditional variance by overcoming the defects of the GARCH family models that can not model the partial features of a process and maintain the benefits of using models The GARCH family describes the fluctuations.
Article Type:
Research/Original Article
Journal of Securities Exchange, Volume:12 Issue:47, 2019
110 - 127  
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