monash_2332.pdf (739.42 kB)

Bayesian Analysis of the Stochastic Conditional Duration Model

Download (739.42 kB)
journal contribution
posted on 05.06.2017 by Strickland, Chris M., Forbes, Catherine S., Martin, Gael M.
A Bayesian Markov Chain Monte Carlo methodology is developed for estimating the stochastic conditional duration model. The conditional mean of durations between trades is modelled as a latent stochastic process, with the conditional distribution of durations having positive support. The sampling scheme employed is a hybrid of the Gibbs and Metropolis Hastings algorithms, with the latent vector sampled in blocks. The suggested approach is shown to be preferable to the quasi-maximum likelihood approach, and its mixing speed faster than that of an alternative single-move algorithm. The methodology is illustrated with an application to Australian intraday stock market data.


Year of first publication



Department of Econometrics and Business Statistics