Bayesian Inference of Asymmetric Stochastic Conditional Duration Models

Posted: 31 Mar 2013 Last revised: 1 Jan 2015

See all articles by Zhongxian Men

Zhongxian Men

Independent

Adam Kolkiewicz

Independent

Tony S. Wirjanto

University of Waterloo - School of Accounting and Finance; University of Waterloo, Department of Statistics & Actuarial Science

Date Written: December 31, 2014

Abstract

This paper extends a stochastic conditional duration (SCD) model for financial transaction data to allow for correlation between error processes or innovations of observed duration process and latent log duration process with the aim of improving the statistical fit of the model. Suitable algorithms of Markov Chain Monte Carlo (MCMC) are developed to t the resulting SCD model under various distributional assumptions about the innovation of the measurement equation. Unlike the estimation methods commonly used to estimate the SCD model in the literature, we work with the original specification of the model, without subjecting the observation equation to a logarithmic transformation. Results of simulation studies suggest that our proposed model and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts of the fitted models. Applications to the IBM transaction data allows comparison of our model and method to those existing in the literature.

Keywords: Stochastic Duration, Bayesian Inference, Markov Chain Monte Carlo, Leverage Effect, Acceptance-rejection, Slice Sampler

JEL Classification: C10, C11, C41, G10

Suggested Citation

Men, Zhongxian and Kolkiewicz, Adam and Wirjanto, Tony S., Bayesian Inference of Asymmetric Stochastic Conditional Duration Models (December 31, 2014). Available at SSRN: https://ssrn.com/abstract=2241082

Zhongxian Men

Independent ( email )

Adam Kolkiewicz

Independent ( email )

Tony S. Wirjanto (Contact Author)

University of Waterloo - School of Accounting and Finance ( email )

200 University Avenue West
Waterloo, Ontario N2L 3G1
Canada
519-888-4567 x35210 (Phone)

HOME PAGE: http://https://uwaterloo.ca/statistics-and-actuarial-science/people-profiles/tony-wirjanto

University of Waterloo, Department of Statistics & Actuarial Science ( email )

200 University Avenue West
Waterloo, Ontario N2L 3G1
Canada
519-888-4567 x35210 (Phone)
519-746-1875 (Fax)

HOME PAGE: http://math.uwaterloo.ca/statistics-and-actuarial-science/people-profiles/tony-wirjanto

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
438
PlumX Metrics