The autoregressive--ARCH (AR--ARCH) and autoregressive--GARCH (AR--GARCH) models, which allow for conditional heteroskedasticity and autoregression, reduce to random walk or white noise for some values of the parameters. We consider generalized versions of the AR--ARCH(1) and AR--GARCH(1,1)...

We give a necessary and sufficient condition for a d-dimensional Lévy process to be in the matrix normalized domain of attraction of a d-dimensional normal random vector, as t↓0. This transfers to the Lévy case classical results of Feller, Khinchin, Lévy and Hahn and Klass for random walks....

Recent models of the insurance risk process use a Lévy process to generalise the traditional Cramér–Lundberg compound Poisson model. This paper is concerned with the behaviour of the distributions of the overshoot and undershoots of a high level, for a Lévy process which drifts to −∞...

The discrete-time GARCH methodology which has had such a profound influence on the modelling of heteroscedasticity in time series is intuitively well motivated in capturing many `stylized facts' concerning financial series, and is now almost routinely used in a wide range of situations, often...

We use Australian data to test the Conditional Capital Asset Pricing Model (Jagannathan and Wang, 1996). Our results are generally supportive: the model performs well compared with a number of competing asset pricing models. In contrast to the study by Jagannathan and Wang, however, we find that...