Please use this identifier to cite or link to this item:

Quasi-maximum likelihood estimation in generalized polynomial autoregressive conditional heteroscedasticity models

Tinkl, Fabian
Year of Publication: 
Series/Report no.: 
IWQW Discussion Paper Series 03/2013
In this article consistency and asymptotic normality of the quasi-maximum likelihood esti- mator (QMLE) in the class of polynomial augmented generalized autoregressive conditional heteroscedasticity models (GARCH) is proven. The result extend the results of (Berkes et al., 2003) and (Francq and Zaköian, 2004) of the standard GARCH model to augmented GARCH models introduced by (Duan, 1997) which contains many commonly employed GARCH models as special cases. The conditions for consistency and asymptotic normality are more tractable than the ones discussed in (Straumann and Mikosch, 2006).
asymptotic normality
polynomial augmented GARCH models
quasi-maximum likelihood estimation
Is replaced by the following version: 
Document Type: 
Working Paper

Files in This Item:
There are no files associated with this item.
The document was removed on behalf of the author(s)/ the editor(s) on: May 8, 2013

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.