Exact Maximum Likelihood estimation for the BL-GARCH model under elliptical distributed innovations
In this paper, we discuss the class of Bilinear GATRCH (BL-GARCH) models which are capable of capturing simultaneously two key properties of non-linear time series : volatility clustering and leverage effects. It has been observed often that the marginal distributions of such time series have heavy tails ; thus we examine the BL-GARCH model in a general setting under some non-Normal distributions. We investigate some probabilistic properties of this model and we propose and implement a maximum likelihood estimation (MLE) methodology. To evaluate the small-sample performance of this method for the various models, a Monte Carlo study is conducted. Finally, within-sample estimation properties are studied using S&P 500 daily returns, when the features of interest manifest as volatility clustering and leverage effects.
|Date of creation:||Apr 2008|
|Contact details of provider:|| Postal: 106-112 boulevard de l'Hôpital 75 647 PARIS CEDEX 13|
Phone: + 33 44 07 81 00
Fax: + 33 1 44 07 83 01
Web page: http://centredeconomiesorbonne.univ-paris1.fr/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:mse:cesdoc:b08027. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lucie Label)
If references are entirely missing, you can add them using this form.