Stochastic Conditional Intensity Processes
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. Copyright 2006, Oxford University Press.
Volume (Year): 4 (2006)
Issue (Month): 3 ()
|Contact details of provider:|| Postal: |
Fax: 01865 267 985
Web page: http://jfec.oxfordjournals.org/
More information through EDIRC
|Order Information:||Web: http://www.oup.co.uk/journals|
When requesting a correction, please mention this item's handle: RePEc:oup:jfinec:v:4:y:2006:i:3:p:450-493. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Oxford University Press)or (Christopher F. Baum)
If references are entirely missing, you can add them using this form.