Bayesian Inference of Asymmetric Stochastic Conditional Duration Models

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