Stochastic Conditional Duration Models with Mixture Processes
Posted: 31 Mar 2013 Last revised: 1 Jan 2015
Date Written: December 31, 2014
This paper studies stochastic conditional duration models with a mixture of distribution processes for financial asset's transaction data. The mixture component distributions include exponential, gamma and Weibull. The models allow for a correlation between the observed durations and the logarithm of the conditional expected durations. Suitable MCMC algorithms are developed for Bayesian inference of parameters and duration forecasting of the models. Unlike much of the existing studies in this literature, simulation studies and empirical applications suggest that the proposed models and method are able to t the left tail of the marginal distribution of duration time series relatively well.
Keywords: Stochastic conditional duration, Mixture of distributions, Bayesian inference, Markov Chain Monte Carlo, Leverage effect, Slice sampler
JEL Classification: C10, C11, C41, G10
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