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