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The latent factor VAR model: Testing for a common component in the intraday trading process

Author

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  • Nikolaus Hautsch

    (Institute of Economics, University of Copenhagen)

Abstract

In this paper, we propose a framework for the modelling of multivariate dynamic processes which are driven by an unobservable common autoregressive component. Economically motivated by the mixture-of-distribution hypothesis, we model the multivariate intraday trading process of return volatility, volume and trading intensity by a VAR model that is augmentedby a joint latent factor serving as a proxy for the unobserved information flow. The model is estimated by simulated maximum likelihood using efficient importance sampling techniques. Analyzing intraday data from the NYSE, we find strong empirical evidence for the existence of an underlying persistent component as an important driving force of the trading process. It is shown that the inclusion of the latent factor clearly improves the goodness-of-fit of the model as well as its dynamical and distributional properties.

Suggested Citation

  • Nikolaus Hautsch, 2005. "The latent factor VAR model: Testing for a common component in the intraday trading process," FRU Working Papers 2005/03, University of Copenhagen. Department of Economics. Finance Research Unit.
    • Handle: RePEc:kud:kuiefr:200503
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    File URL: http://www.econ.ku.dk/FRU/WorkingPapers/PDF/2005/2005_03.pdf
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    Citations

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    Cited by:

    1. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 450-493.

    More about this item

    Keywords

    observation vs. parameter driven dynamics; mixture-of-distribution hypothesis; VAR model; efficient importance sampling;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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