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Modeling the Dynamics of Transaction Prices on the NYSE in a Bayesian State-Space Filtering Framework

In: Computing Science and Statistics

Author

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  • Peter J. Kempthorne

    (Massachusetts Institute of Technology, Sloan School of Management)

  • Arnout M. Eikeboom

    (Massachusetts Institute of Technology, Sloan School of Management)

Abstract

Traditional models for the dynamics of stock prices are discrete-time, univariate and cross-sectional time series based on monthly, weekly, and daily data with continuous-valued sample spaces. With intraday data at the transaction level, these models are inappropriate. We can no longer assume that prices change in a continuous fashion. Instead prices trade at discrete increments of monetary value, a tick, which is $0,125 for the New York Stock Exchange (NYSE). Also, stocks trade at unequally-spaced intervals of time which can vary considerably over the trading day.

Suggested Citation

  • Peter J. Kempthorne & Arnout M. Eikeboom, 1992. "Modeling the Dynamics of Transaction Prices on the NYSE in a Bayesian State-Space Filtering Framework," Springer Books, in: Connie Page & Raoul LePage (ed.), Computing Science and Statistics, pages 179-185, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4612-2856-1_23
    DOI: 10.1007/978-1-4612-2856-1_23
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