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Journal of Financial Econometrics Advance Access originally published online on May 12, 2006
Journal of Financial Econometrics 2006 4(3):450-493; doi:10.1093/jjfinec/nbj013
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Stochastic Conditional Intensity Processes

Luc Bauwens
     CORE and Department of Economics, Université Catholique de Louvain

Nikolaus Hautsch
     Department of Economics, University of Copenhagen

Address correspondence to Nikolaus Hautsch, Department of Economics, Studiestraede 6, University of Copenhagen, DK-1455 Copenhagen K, Denmark, or e-mail: nikolaus.hautsch{at}econ.ku.dk.

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

Received October 10, 2005; revised March 27, 2006; accepted March 29, 2006


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