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2013 (Vol. 5, Issue: 02)
Article Information:

Improving Forecasts of Generalized Autoregressive Conditional Heteroskedasticity with Wavelet Transform

Yu Zhao, Xiaoming Zou and Hong Xu
Corresponding Author:  Yu Zhao 

Key words:  Brent oil, daily returns, DWT-GARCH, GARCH, volatility, wavelet transform,
Vol. 5 , (02): 649-653
Submitted Accepted Published
May 24, 2012 July 09, 2012 January 11, 2013
Abstract:

In the study, we discussed the generalized autoregressive conditional heteroskedasticity model and enhanced it with wavelet transform to evaluate the daily returns for 1/4/2002-30/12/2011 period in Brent oil market. We proposed discrete wavelet transform generalized autoregressive conditional heteroskedasticity model to increase the forecasting performance of the generalized autoregressive conditional heteroskedasticity model. Our new approach can overcome the defect of generalized autoregressive conditional heteroskedasticity family models which canít describe the detail and partial features of times series and retain the advantages of them at the same time. Comparing with the generalized autoregressive conditional heteroskedasticity model, the new approach significantly improved forecast results and greatly reduces conditional variances.
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  Cite this Reference:
Yu Zhao, Xiaoming Zou and Hong Xu, 2013. Improving Forecasts of Generalized Autoregressive Conditional Heteroskedasticity with Wavelet Transform.  Research Journal of Applied Sciences, Engineering and Technology, 5(02): 649-653.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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