BL-GARCH model with 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:||Jul 2010|
|Date of revision:|
|Note:||View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00368340|
|Contact details of provider:|| Web page: https://hal.archives-ouvertes.fr/|
When requesting a correction, please mention this item's handle: RePEc:hal:cesptp:halshs-00368340. 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: (CCSD)
If references are entirely missing, you can add them using this form.