Stochastic Conditional Intensity Processes
Posted: 29 Feb 2008
Date Written: 2006
In this article, we introduce the so-called stochastic conditional intensity (SCI) model by extending Russell's (1999) autoregressive conditional intensity (ACI) model by a latent common dynamic factor that jointly drives the individual intensity components. We show by simulations that the proposed model allows for a wide range of (cross-)autocorrelation structures in multivariate point processes. The model is estimated by simulated maximum likelihood (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 and show that its inclusion allows for an improved and more parsimonious specification of the multivariate intensity process.
Keywords: conditional intensity function, efficient importance sampling, multivariate point processes, parameter-driven and observation-driven models, price intensities
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