| Abstract: | In this article, we introduce the so-called stochastic conditionalintensity (SCI) model by extending Russell’s (1999) autoregressiveconditional intensity (ACI) model by a latent common dynamicfactor that jointly drives the individual intensity components.We show by simulations that the proposed model allows for awide range of (cross-)autocorrelation structures in multivariatepoint processes. The model is estimated by simulated maximumlikelihood (SML) using the efficient importance sampling (EIS)technique. By modeling price intensities based on NYSE trading,we provide significant evidence for a joint latent factor andshow that its inclusion allows for an improved and more parsimoniousspecification of the multivariate intensity process. |
