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Bayesian Analysis of the Stochastic Conditional Duration Model

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  • Chris M. Strickland
  • Catherine S. Forbes

    ()

  • Gael M. Martin

    ()

Abstract

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.

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File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2003/wp14-03.pdf
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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 14/03.

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Length: 28 pages
Date of creation: Aug 2003
Date of revision:
Handle: RePEc:msh:ebswps:2003-14

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Keywords: Transaction data; Latent factor model; Non-Gaussian state space model; Kalman filter and simulation smoother.;

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