Comparative Study of Estimation of the Asymmetric in Conditional Variance Using EGARCH Models and CWN Model | Chapter 10 | Mathematics and Computer Science: Contemporary Developments Vol. 7

The aim of the study is to compare the asymmetry in the conditional variance of Exponential Generalized Autoregression Conditional Heteroscdastiicity (EGARCH) with the Combine White Noise (CWN) model to acquire reliable results. The EGARCH has high information criteria and low log likelihood while CWN has minimum information criteria and high log likelihood which makes CWN a more suitable estimation. CWN estimation is more efficient than EGARCH estimation when employing the determinant covariance matrix values. Minimum forecast error in CWN revealed better forecast accuracy when compared with EGARCH. Therefore, CWN estimation results have revealed more efficiency than the EGARCH model estimation in the overall results.

 

Author (s) Details

 

Ayodele Abraham Agboluaje
Department of Mathematical Sciences, Faculty of Natural Sciences, Ibrahim Badamasi Babangida University, Lapai, Nigeria and School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Malaysia.

 

Suzilah Bt Ismail
School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Malaysia.

 

Chee Yin Yip
Department of Economics, Faculty of Business and Finance, Universiti Tuanku Abdul Rahman, Malaysia.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v7/2431

Improving GDP Error Term Modeling: Application of the Combine White Noise Model to Australia | Chapter 6 | Science and Technology: Developments and Applications Vol. 5

The objective of the study is to improve the Gross Domestic Product (GDP) error term modeling, with the Combine White Noise (CWN) Model in Australia. The estimation of the Combine White Noise model passes the stability condition, stationary, serial correlation, and the CWN model estimation yields the best results with minimum information criteria and higher log-likelihood values than EGARCH  and VAR models. The determinant of the residual of the covariance matrix value indicates that CWN is efficient while the determinant of the residual of the covariance matrix value indicates that VAR is not efficient. CWN has the least forecast errors which are indications of the best results when compared with the EGARCH and VAR models dynamic evaluation forecast errors. The minimum forecast error values indicate forecast accuracy. The total results testified that CWN is the right model. To model conditional heteroscedasticity data with leverage effect in Australia and other nations powerfully, CWN is acclaimed. The contribution of this study to the scientific community is that the CWN gives suitable results that improve the weaknesses of the existing models. The CWN forecast output is more reasonable for effective policy making. Implementation of this CWN will boost the economy of the society.

 

Author (s) Details

 

Ayodele Abraham Agboluaje
Department of Mathematics Sciences, Faculty of Natural Sciences, Ibrahim Badamasi Babangida University, Lapai, Nigeria and School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Malaysia.

 

Suzilah Ismail
School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Malaysia.

 

Chee Yin Yip
Department of Economics, Faculty of Business and Finance, Universiti Tuanku Abdul Rahman, Malaysia.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v5/4274