A Note on GARCH(1,1) Estimation via Different Estimation Methods

摘要

Generalized autoregressive conditional heteroscedasticity (GARCH) model has earned large success for its competitiveness and parsimony in the financial econometric literature. GARCH model is based on the infinite ARCH specification term that reduces the number of estimated parameters from infinity to two. This paper analyzes estimation bias for different GARCH specification in various sample sizes. Furthermore, we employ generalized method of moments and maximum likelihood framework for estimation evaluation. We show that selected estimation methods yields to different biases under various sample sizes assumption. Finally, our results suggest that maximum likelihood estimation gives better estimates than generalized method of moments.