Stochastic Conditional Duration Models with Mixture Processes

Tony Wirjanto (twirjant@uwaterloo.ca), Adam W. Kolkiewicz and Zhongxian Men
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Adam W. Kolkiewicz: Department of Statistics and Actuarial Science, University of Waterloo, Canada
Zhongxian Men: Department of Statistics and Actuarial Science, University of Waterloo, Canada

Working Paper series from Rimini Centre for Economic Analysis

Abstract: This paper studies a stochastic conditional duration (SCD) model with a mixture of distribution processes for financial asset’s transaction data. Specifically it imposes a mixture of two positive distributions on the innovations of the observed duration process, where the mixture component distributions could be either Exponential, Gamma or Weibull. The model also allows for correlation between the observed durations and the logarithm of the latent conditionally expected durations in order to capture a leverage effect known to exist in the equity market. In addition the proposed mixture SCD model is shown to be able to accommodate possibly heavy tails of the marginal distribution of durations. Novel Markov Chain Monte Carlo (MCMC) algorithms are developed for Bayesian inference of parameters and duration forecasting of these models. Simulation studies and empirical applications to two stock duration data sets are provided to assess the performance of the proposed mixture SCD models and the accompanying MCMC algorithms.

Keywords: Stochastic conditional duration; Mixture of distributions; Bayesian inference; Markov Chain Monte Carlo; Leverage effect; Slice sampler (search for similar items in EconPapers)
Date: 2013-05
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:29_13

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