Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/32170
Authors: 
Bien, Katarzyna
Nolte, Ingmar
Pohlmeier, Winfried
Year of Publication: 
2007
Series/Report no.: 
Discussion paper series // Zentrum für Finanzen und Ökonometrie, Universität Konstanz 2007,04
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.
Subjects: 
Multivariate Discrete Distributions
Conditional Inflation
Copula Functions
Truncations
Metropolized-Independence Sampler
JEL: 
G10
F30
C30
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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