Stochastic Conditional Intensity Processes

Posted: 29 Feb 2008

See all articles by Nikolaus Hautsch

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research; Center for Financial Studies (CFS); Vienna Graduate School of Finance (VGSF)

Luc Bauwens

Université catholique de Louvain

Date Written: 2006

Abstract

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

Suggested Citation

Hautsch, Nikolaus and Bauwens, Luc, Stochastic Conditional Intensity Processes ( 2006). Journal of Financial Econometrics, Vol. 4, Issue 3, pp. 450-493, 2006. Available at SSRN: https://ssrn.com/abstract=1096981 or http://dx.doi.org/10.1093/jjfinec/nbj013

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research ( email )

Oskar-Morgenstern-Platz 1
Vienna, A-1090
Austria

Center for Financial Studies (CFS) ( email )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

Luc Bauwens (Contact Author)

Université catholique de Louvain ( email )

CORE
34 Voie du Roman Pays
B-1348 Louvain-la-Neuve, b-1348
Belgium
32 10 474321 (Phone)
32 10 474301 (Fax)

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