Bayesian Analysis of the Stochastic Conditional Duration Model

Chris M. Strickland, Catherine Forbes and Gael Martin

No 14/03, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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.

Keywords: Transaction data; Latent factor model; Non-Gaussian state space model; Kalman filter and simulation smoother. (search for similar items in EconPapers)
JEL-codes: C11 C15 C41 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2003-08
New Economics Papers: this item is included in nep-cmp and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Bayesian analysis of the stochastic conditional duration model (2006)
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