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Bayesian analysis of the stochastic conditional duration model

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  • Strickland, Chris M.
  • Forbes, Catherine S.
  • Martin, Gael M.

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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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 50 (2006)
Issue (Month): 9 (May)
Pages: 2247-2267

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Handle: RePEc:eee:csdana:v:50:y:2006:i:9:p:2247-2267

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