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Autoregressive Discrete Processes and Quote Dynamics

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Econometrics of Financial High-Frequency Data

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Abstract

In this chapter, we discuss dynamic models for discrete-valued data and quote processes. As illustrated in Chap. 4, the time series of the number of events in a given time interval yields a counting process and provides an alternative way to characterize the underlying point process. Section 13.1 presents a class of univariate autoregressive models for count data based on dynamic parameterizations of the conditional mean function in a Poisson distribution. Moreover, we discuss extensions thereof, such as the Negative Binomial distribution and Double Poisson distribution.

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Notes

  1. 1.

    See, for instance, Groß-Klußmann and Hautsch (2011b) for an application to estimate high-frequency market responses to publications of automated news feeds.

  2. 2.

    For an analysis of the effects of neglected discreteness, see Harris (1990), Gottlieb and Kalay (1985) or Ball (1988).

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Authors and Affiliations

  1. Institute for Statistics and Econometrics School of Business and Economics, Humboldt-Universität zu Berlin, Spandauer Str. 1, 10178, Berlin, Germany

    Professor Dr. Nikolaus Hautsch

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  1. Professor Dr. Nikolaus Hautsch
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Correspondence to Nikolaus Hautsch .

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Hautsch, N. (2012). Autoregressive Discrete Processes and Quote Dynamics. In: Econometrics of Financial High-Frequency Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21925-2_13

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  • DOI: https://doi.org/10.1007/978-3-642-21925-2_13

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