An Inflated Multivariate Integer Count Hurdle Model: An Application to Bid and Ask Quote Dynamics

Katarzyna Bien (), Ingmar Nolte () and Winfried Pohlmeier ()
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Katarzyna Bien: University of Konstanz

No 07-04, CoFE Discussion Paper from Center of Finance and Econometrics, University of Konstanz

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.

Keywords: Multivariate Discrete Distributions; Conditional Inflation; Copula Functions; Truncations; Metropolized-Independence Sampler (search for similar items in EconPapers)
JEL-codes: G10 F30 C30 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-mst
Date: 2007-03-28
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