Asymmetric Stochastic Conditional Duration Model--A Mixture-of-Normal Approach
John Knight and
Tony Wirjanto ()
Journal of Financial Econometrics, 2011, vol. 9, issue 3, 469-488
This paper extends the stochastic conditional duration model first proposed by Bauwens and Veredas (2004) by imposing mixtures of bivariate normal distributions on the innovations of the observation and latent equations of the duration process. This extension allows the model not only to capture various density shapes of the durations but also to easily accommodate a richer dependence structure between the two innovations. In addition, it applies an estimation methodology based on the empirical characteristic function. Empirical applications based on the IBM and Boeing transaction data are provided to assess and illustrate the performance of the proposed model and the estimation method. One interesting empirical finding in this paper is that there is a significantly positive correlation under both the contemporaneous and lagged intertemporal dependence structures for the IBM and Boeing duration data. Copyright The Author 2011. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org, Oxford University Press.
References: Add references at CitEc
Citations: View citations in EconPapers (6) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
Working Paper: Asymmetric Stochastic Conditional Duration Model --A Mixture of Normals Approach" (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:oup:jfinec:v:9:y:2011:i:3:p:469-488
Ordering information: This journal article can be ordered from
Access Statistics for this article
Journal of Financial Econometrics is currently edited by RenÈ Garcia and Eric Renault
More articles in Journal of Financial Econometrics from Society for Financial Econometrics Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().