An inflated Multivariate Integer Count Hurdle model: An application to bid and ask quote dynamics

Katarzyna Bień-Barkowska, Ingmar Nolte and Winfried Pohlmeier

No 07/04, CoFE Discussion Papers from University of Konstanz, Center of Finance and Econometrics (CoFE)

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 bivariate 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: C30 F30 G10 (search for similar items in EconPapers)
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cofedp:0704

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