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Abstract
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Article Information:
Improving Forecasts of Generalized Autoregressive Conditional Heteroskedasticity with Wavelet Transform
Yu Zhao, Xiaoming Zou and Hong Xu
Corresponding Author: Yu Zhao
Submitted: May 24, 2012
Accepted: July 09, 2012
Published: January 11, 2013 |
Abstract:
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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
can’t 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.
Key words: Brent oil, daily returns, DWT-GARCH, GARCH, volatility, wavelet transform,
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Cite this Reference:
Yu Zhao, Xiaoming Zou and Hong Xu, . Improving Forecasts of Generalized Autoregressive Conditional Heteroskedasticity with Wavelet Transform. Research Journal of Applied Sciences, Engineering and Technology, (02): 649-653.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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