TY  - JOUR
T1  - Aperformance Ratings of an Autocovariance Base Estimator (ABE) in the Estimation of GARCH Model Parameters When the Normality Assumption is Invalid
AU - Eni, Daniel 
JO  - Research Journal of Applied Sciences
VL  - 5
IS  - 2
SP  - 108
EP  - 111
PY  - 2010
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2010.108.111
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2010.108.111
KW  - Autocovariance functions
KW  -parameter estimation
KW  -normality
KW  -probabibilty
KW  -distribution GARCH
KW  -invalid
AB  - In this study, the performance of an Autocovariance Base Estimator (ABE) for GARCH models was studied, against that of the Maximum Likelihood Estimator (MLE) if the distribution assumption is wrongly specified as normal. We do this by first simulating time series data that fits GARCH model using the Log normal and t-distribution with degrees of freedom of 5, 10 and 15 as the true probability distribution but assumed normality in the process of parameter estimations. To keep track of consistency, we conduct and present the studies in sample sizes of 200, 500, 1000 and  1200. The two methods were then used to analyse the series under normality assumption. The result shows that the ABE method appears to be competitive in the situations considered.
ER  -