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:|
|Publication status:||Published in Journal of Statistical Computation and Simulation, Taylor & Francis, 2010, 80 (7), pp.775-791. <10.1080/00949650902773577>|
|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/|
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