A MULTISCALE STOCHASTIC CONDITIONAL DURATION MODEL

Zhongxian Men, Tony S. Wirjanto and Adam W. Kolkiewicz
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Zhongxian Men: Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1, Canada
Tony S. Wirjanto: Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1, Canada†School of Accounting and Finance, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1, Canada
Adam W. Kolkiewicz: Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1, Canada

Annals of Financial Economics (AFE), 2016, vol. 11, issue 04, 1-28

Abstract: This paper studies a stochastic conditional duration model running on multiple time scales with the aim of better capturing the dynamics of a duration process of financial transaction data. New Markov chain Monte Carlo (MCMC) algorithms are developed for the model under three distributional assumptions about the innovation of the measurement equation for a two-component model. Simulation results suggest that the proposed model and MCMC method improve in-sample fits and duration forecasts. Most importantly applications to FIAT and IBM duration datasets indicate the existence of at least two factors (or components) governing the dynamics of the financial duration process.

Keywords: Markov chain Monte Carlo; multiscale; auxiliary particle filter; probability integral transform; deviance information criterion (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1142/S2010495216500202

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