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

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An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics. / Bien, Katarzyna; Nolte, Ingmar; Pohlmeier, Winfried.

In: Journal of Applied Econometrics, Vol. 26, No. 4, 06.2011, p. 669-707.

Research output: Contribution to journalJournal article

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Bien, K, Nolte, I & Pohlmeier, W 2011, 'An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics', Journal of Applied Econometrics, vol. 26, no. 4, pp. 669-707. https://doi.org/10.1002/jae.1122

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Bien, Katarzyna ; Nolte, Ingmar ; Pohlmeier, Winfried. / An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics. In: Journal of Applied Econometrics. 2011 ; Vol. 26, No. 4. pp. 669-707.

Bibtex

@article{0ecfa895cbcb4ec7b59011d63659afab,
title = "An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics",
abstract = "In this paper we develop a model for the conditional inflated multivariate density of integer count variables with domain ℤn, n ∈ ℕ. 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 kinds of properties can be found for high- or ultra-high-frequency data describing the trading process on financial markets. We present a straightforward sampling method for 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. Copyright {\textcopyright} 2009 John Wiley & Sons, Ltd.",
author = "Katarzyna Bien and Ingmar Nolte and Winfried Pohlmeier",
year = "2011",
month = jun,
doi = "10.1002/jae.1122",
language = "English",
volume = "26",
pages = "669--707",
journal = "Journal of Applied Econometrics",
issn = "0883-7252",
publisher = "John Wiley and Sons Ltd",
number = "4",

}

RIS

TY - JOUR

T1 - An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics

AU - Bien, Katarzyna

AU - Nolte, Ingmar

AU - Pohlmeier, Winfried

PY - 2011/6

Y1 - 2011/6

N2 - In this paper we develop a model for the conditional inflated multivariate density of integer count variables with domain ℤn, n ∈ ℕ. 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 kinds of properties can be found for high- or ultra-high-frequency data describing the trading process on financial markets. We present a straightforward sampling method for 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. Copyright © 2009 John Wiley & Sons, Ltd.

AB - In this paper we develop a model for the conditional inflated multivariate density of integer count variables with domain ℤn, n ∈ ℕ. 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 kinds of properties can be found for high- or ultra-high-frequency data describing the trading process on financial markets. We present a straightforward sampling method for 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. Copyright © 2009 John Wiley & Sons, Ltd.

U2 - 10.1002/jae.1122

DO - 10.1002/jae.1122

M3 - Journal article

VL - 26

SP - 669

EP - 707

JO - Journal of Applied Econometrics

JF - Journal of Applied Econometrics

SN - 0883-7252

IS - 4

ER -