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A Threshold Stochastic Conditional Duration Model for Financial Transaction Data

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Author Info

  • Zhongxian Men

    (Department of Statistics & Actuarial Science, University of Waterloo, Canada)

  • Tony S. Wirjanto

    ()
    (Department of Statistics & Actuarial Science, University of Waterloo, Canada; School of Accounting and Finance, University of Waterloo, Canada)

  • Adam W. Kolkiewicz

    (Department of Statistics & Actuarial Science, University of Waterloo, Canada)

Abstract

This paper proposes a threshold stochastic conditional duration (TSCD) model to capture the asymmetric property of financial transactions. The innovation of the observable duration equation is assumed to follow a threshold distribution with two component distributions switching between two regimes. The distributions in different regimes are assumed to be Exponential, Gamma or Weibull. To account for uncertainty in the unobserved threshold level, the observed durations are treated as self-exciting threshold variables. Adopting a Bayesian approach, we develop novel Markov Chain Monte Carlo algorithms to estimate all of the unknown parameters and latent states. To forecast the one-step ahead durations, we employ an auxiliary particle filter where the filter and prediction distributions of the latent states are approximated. The proposed model and the developed MCMC algorithms are illustrated by using both simulated and actual financial transaction data. For model selection, a Bayesian deviance information criterion is calculated to compare our model with other competing models in the literature. Overall, we find that the threshold SCD model performs better than the SCD model when a single positive distribution is assumed for the innovation of the duration equation.

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File URL: http://www.rcfea.org/RePEc/pdf/wp30_13.pdf
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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 30_13.

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Date of creation: May 2013
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Handle: RePEc:rim:rimwps:30_13

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Related research

Keywords: Stochastic conditional duration; Threshold; Markov Chain Monte Carlo; Auxiliary particle filter; Deviance information criterion;

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Cited by:
  1. Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2013. "Bayesian Inference of Multiscale Stochastic Conditional Duration Models," Working Paper Series 63_13, The Rimini Centre for Economic Analysis.

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