Research Article Open Access

Modeling the Error Term by Moving Average and Generalized Autoregressive Conditional Heteroscedasticity Processes

Ayodele Abraham Agboluaje1, Suzilah bt Ismail2 and Chee Yin Yip3
  • 1 Department of Mathematics and Computer Science, Faculty of Natural Sciences, Ibrahim Badamasi Babangida University, Lapai, Nigeria
  • 2 School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Malaysia
  • 3 Department of Economics, Faculty of Business and Finance, Universiti Tuanku Abdul Rahman, Malaysia

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.

American Journal of Applied Sciences
Volume 12 No. 11, 2015, 896-901

DOI: https://doi.org/10.3844/ajassp.2015.896.901

Submitted On: 26 September 2015 Published On: 20 November 2015

How to Cite: Agboluaje, A. A., Ismail, S. B. & Yip, C. Y. (2015). Modeling the Error Term by Moving Average and Generalized Autoregressive Conditional Heteroscedasticity Processes. American Journal of Applied Sciences, 12(11), 896-901. https://doi.org/10.3844/ajassp.2015.896.901

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Keywords

  • Determinant Residual Covariance
  • Minimum Forecast Errors
  • Minimum Information Criteria
  • Leverage
  • Log Likelihood