Modeling the Error Term by Moving Average and Generalized Autoregressive Conditional Heteroscedasticity Processes
Ayodele Abraham Agboluaje, Suzilah bt Ismail and Chee Yin Yip
DOI : 10.3844/ajassp.2015.896.901
American Journal of Applied Sciences
Volume 12, Issue 11
This study has been able to reveal that the Combine White Noise model outperforms the existing Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Moving Average (MA) models in modeling the errors, that exhibits conditional heteroscedasticity and leverage effect. MA process cannot model the data that reveals conditional heteroscedasticity and GARCH cannot model the leverage effect also. The standardized residuals of GARCH errors are decomposed into series of white noise, modeled to be Combine White Noise model (CWN). CWN model estimation yields best results with minimum information criteria and high log likelihood values. While the EGARCH model estimation yields better results of minimum information criteria and high log likelihood values when compare with MA model. CWN has the minimum forecast errors which are indications of best results when compare with the GARCH and MA models dynamic evaluation forecast errors. Every result of CWN outperforms the results of both GARCH and MA.
© 2015 Ayodele Abraham Agboluaje, Suzilah bt Ismail and Chee Yin Yip. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.