Agboluaje, Ayodele Abraham and Ismail, Suzilah and Chee Yin, Yip (2015) Modeling the Error Term by Moving Average and Generalized Autoregressive Conditional Heteroscedasticity Processes. American Journal of Applied Sciences, 12 (11). pp. 896-901. ISSN 1546-9239

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Abstract

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

Item Type:	Article
Uncontrolled Keywords:	Determinant Residual Covariance, Minimum Forecast Errors, Minimum Information Criteria, Leverage, Log Likelihood
Subjects:	Q Science > QA Mathematics
Divisions:	School of Quantitative Sciences
Depositing User:	Mdm. Sarkina Mat Saad @ Shaari
Date Deposited:	04 Jul 2024 03:25
Last Modified:	04 Jul 2024 03:25
URI:	https://repo.uum.edu.my/id/eprint/30981

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