An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics
AbstractIn 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 InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.
Volume (Year): 26 (2011)
Issue (Month): 4 (06)
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Web page: http://www.interscience.wiley.com/jpages/0883-7252/
Other versions of this item:
- Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2007. "An Inflated Multivariate Integer Count Hurdle Model: An Application to Bid and Ask Quote Dynamics," CoFE Discussion Paper 07-04, Center of Finance and Econometrics, University of Konstanz.
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- F30 - International Economics - - International Finance - - - General
- C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
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