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Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity

Robert Jung (), Roman Liesenfeld and Jean-Francois Richard

No 2008-12, Economics Working Papers from Christian-Albrechts-University of Kiel, Department of Economics

Abstract: We propose a dynamic factor model for the analysis of multivariate time series count data. Our model allows for idiosyncratic as well as common serially correlated latent factors in order to account for potentially complex dynamic interdependence between series of counts. The model is estimated under alternative count distributions (Poisson and negative binomial). Maximum Likelihood estimation requires high-dimensional numerical integration in order to marginalize the joint distribution with respect to the unobserved dynamic factors. We rely upon the Monte-Carlo integration procedure known as Efficient Importance Sampling which produces fast and numerically accurate estimates of the likelihood function. The model is applied to time series data consisting of numbers of trades in 5 minutes intervals for five NYSE stocks from two industrial sectors. The estimated model accounts for all key dynamic and distributional features of the data. We find strong evidence of a common factor which we interpret as reflecting market-wide news. In contrast, sector-specific factors are found to be statistically insignifficant.

Keywords: Dynamic latent variables; Importance sampling; Mixture of distribution models; Poisson distribution; Simulated Maximum Likelihood (search for similar items in EconPapers)
Date: 2008
New Economics Papers: this item is included in nep-ecm and nep-mst
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Related works:
Journal Article: Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity (2011) Downloads
Journal Article: Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity (2011) Downloads
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