BAYESIAN MODEL SELECTION VIA FILTERING FOR A CLASS OF MICRO-MOVEMENT MODELS OF ASSET PRICE

Michael A. Kouritzin () and Yong Zeng ()
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Michael A. Kouritzin: Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta T6G 2G1, Canada
Yong Zeng: Department of Mathematics and Statistics, University of Missouri at Kansas City, Kansas City, MO 64110, USA

International Journal of Theoretical and Applied Finance (IJTAF), 2005, vol. 08, issue 01, 97-121

Abstract: This paper develops the Bayesian model selection based on Bayes factor for a rich class of partially-observed micro-movement models of asset price. We focus on one recursive algorithm to calculate the Bayes factors, first deriving the system of SDEs for them and then applying the Markov chain approximation method to yield a recursive algorithm. We prove the consistency (or robustness) of the recursive algorithm. To illustrate the construction of such a recursive algorithm, we consider a model selection problem for two micro-movement models with and without stochastic volatility, and provide simulation and real-data examples to demonstrate the effectiveness of the Bayes factor in the model selection for this class of models.

Keywords: Bayes factor; counting process; filtering; Markov chain approximation method; model selection; price clustering; Transaction data (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (4)

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DOI: 10.1142/S0219024905002883

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