Bayesian Analysis of the Stochastic Conditional Duration Model
journal contributionposted on 05.06.2017 by Strickland, Chris M., Forbes, Catherine S., Martin, Gael M.
Any type of content formally published in an academic journal, usually following a peer-review process.
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.