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An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics

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  • Katarzyna Bien
  • Ingmar Nolte
  • Winfried Pohlmeier

Abstract

In this paper we develop a model for the conditional inflated multivariate density of integer count variables with domain Zn. Our modelling framework is based on a copula approach and can be used for a broad set of applications where the primary characteristics of the data are: (i) discrete domain, (ii) the tendency to cluster at certain outcome values and (iii) contemporaneous dependence. These kind of properties can be found for high or ultra-high frequent data describing the trading process on financial markets. We present a straightforward method of sampling from such an inflated multivariate density through the application of an Independence Metropolis-Hastings sampling algorithm. We demonstrate the power of our approach by modelling the conditional bivari- ate density of bid and ask quote changes in a high frequency setup. We show how to derive the implied conditional discrete density of the bid-ask spread, taking quote clusterings (at multiples of 5 ticks) into account.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 26 (2011)
Issue (Month): 4 (06)
Pages: 669-707

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Handle: RePEc:wly:japmet:v:26:y:2011:i:4:p:669-707

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References

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Cited by:
  1. Katarzyna BieĊ„-Barkowska, 2012. "A Bivariate Copula-based Model for a Mixed Binary-Continuous Distribution: A Time Series Approach," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 4(2), pages 117-142, June.
  2. Partha Deb & Pravin K. Trivedi & David M. Zimmer, 2009. "Dynamic Cost-offsets of Prescription Drug Expenditures: Panel Data Analysis Using a Copula-based Hurdle Model," NBER Working Papers 15191, National Bureau of Economic Research, Inc.

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