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Modeling a multivariate transaction process

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Modeling a multivariate transaction process. / Nolte, Ingmar.

In: Journal of Financial Econometrics, Vol. 6, No. 1, 2008, p. 143-170.

Research output: Contribution to journalJournal article

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Nolte, I 2008, 'Modeling a multivariate transaction process', Journal of Financial Econometrics, vol. 6, no. 1, pp. 143-170. https://doi.org/10.1093/jjfinec/nbm020

APA

Nolte, I. (2008). Modeling a multivariate transaction process. Journal of Financial Econometrics, 6(1), 143-170. https://doi.org/10.1093/jjfinec/nbm020

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Nolte, Ingmar. / Modeling a multivariate transaction process. In: Journal of Financial Econometrics. 2008 ; Vol. 6, No. 1. pp. 143-170.

Bibtex

@article{de7c46b4926046ab8bb9b59b2c280aa3,
title = "Modeling a multivariate transaction process",
abstract = "In this paper the dynamics of a joint transaction process are investigated. The transaction process is characterized by four marks: price changes, transaction volumes, bid–ask spreads and intertrade durations. Based on a copula approach, a model for their joint density is proposed, which avoids forcing a priori assumptions on the instantaneous causality relationships between the four variables as necessary in decomposition models, where the joint density is decomposed into its conditional and unconditional densities. The price change process is treated as a discrete process and specified with an integer count hurdle model and the transaction volumes, bid–ask spreads, and trade durations processes are modeled along the lines of fractionally integrated autoregressive conditional models, which are suited very well to capture the high persistency, empirically observed in these processes. The model is applied to three stocks traded at the New York Stock Exchange (NYSE) in May, 2001 and we investigate several market microstructure hypotheses in the empirical part of this paper. ",
keywords = "copula functions, discrete price changes, fractionally integrated autoregressive conditional duration models, integer count hurdle model, market microstructure, transaction data",
author = "Ingmar Nolte",
year = "2008",
doi = "10.1093/jjfinec/nbm020",
language = "English",
volume = "6",
pages = "143--170",
journal = "Journal of Financial Econometrics",
issn = "1479-8409",
publisher = "Oxford University Press",
number = "1",

}

RIS

TY - JOUR

T1 - Modeling a multivariate transaction process

AU - Nolte, Ingmar

PY - 2008

Y1 - 2008

N2 - In this paper the dynamics of a joint transaction process are investigated. The transaction process is characterized by four marks: price changes, transaction volumes, bid–ask spreads and intertrade durations. Based on a copula approach, a model for their joint density is proposed, which avoids forcing a priori assumptions on the instantaneous causality relationships between the four variables as necessary in decomposition models, where the joint density is decomposed into its conditional and unconditional densities. The price change process is treated as a discrete process and specified with an integer count hurdle model and the transaction volumes, bid–ask spreads, and trade durations processes are modeled along the lines of fractionally integrated autoregressive conditional models, which are suited very well to capture the high persistency, empirically observed in these processes. The model is applied to three stocks traded at the New York Stock Exchange (NYSE) in May, 2001 and we investigate several market microstructure hypotheses in the empirical part of this paper.

AB - In this paper the dynamics of a joint transaction process are investigated. The transaction process is characterized by four marks: price changes, transaction volumes, bid–ask spreads and intertrade durations. Based on a copula approach, a model for their joint density is proposed, which avoids forcing a priori assumptions on the instantaneous causality relationships between the four variables as necessary in decomposition models, where the joint density is decomposed into its conditional and unconditional densities. The price change process is treated as a discrete process and specified with an integer count hurdle model and the transaction volumes, bid–ask spreads, and trade durations processes are modeled along the lines of fractionally integrated autoregressive conditional models, which are suited very well to capture the high persistency, empirically observed in these processes. The model is applied to three stocks traded at the New York Stock Exchange (NYSE) in May, 2001 and we investigate several market microstructure hypotheses in the empirical part of this paper.

KW - copula functions

KW - discrete price changes

KW - fractionally integrated autoregressive conditional duration models

KW - integer count hurdle model

KW - market microstructure

KW - transaction data

U2 - 10.1093/jjfinec/nbm020

DO - 10.1093/jjfinec/nbm020

M3 - Journal article

VL - 6

SP - 143

EP - 170

JO - Journal of Financial Econometrics

JF - Journal of Financial Econometrics

SN - 1479-8409

IS - 1

ER -