Home > Research > Publications & Outputs > Modelling financial transaction price movements
View graph of relations

Modelling financial transaction price movements: a dynamic integer count data model

Research output: Contribution to journalJournal articlepeer-review

Published

Standard

Modelling financial transaction price movements : a dynamic integer count data model. / Liesenfeld, Roman; Nolte, Ingmar; Pohlmeier, Winfried.

In: Empirical Economics, Vol. 30, No. 4, 01.2006, p. 795-825.

Research output: Contribution to journalJournal articlepeer-review

Harvard

APA

Vancouver

Author

Liesenfeld, Roman ; Nolte, Ingmar ; Pohlmeier, Winfried. / Modelling financial transaction price movements : a dynamic integer count data model. In: Empirical Economics. 2006 ; Vol. 30, No. 4. pp. 795-825.

Bibtex

@article{bcfb09a4731c47098ac9845c2574cc61,
title = "Modelling financial transaction price movements: a dynamic integer count data model",
abstract = "In this paper we develop a dynamic model for integer counts to capture fundamental properties of financial prices at the transaction level. Our model relies on an autoregressive multinomial component for the direction of the price change and a dynamic count data component for the size of the price changes. Since the model is capable of capturing a wide range of discrete price movements it is particularly suited for financial markets where the trading intensity is moderate or low. We present the model at work by applying it to transaction data of two shares traded at the NYSE traded over a period of one trading month. We show that the model is well suited to test some theoretical implications of the market microstructure theory on the relationship between price movements and other marks of the trading process. Based on density forecast methods modified for the case of discrete random variables we show that our model is capable to explain large parts of the observed distribution of price changes at the transaction level.",
keywords = "financial transaction prices, autoregressive conditional multinomial model, GLARMA, count data, market microstructure effects, TRADING VOLUME, STOCK-PRICES, VOLATILITY, INFORMATION, CONSTRAINTS, ADJUSTMENT, MARKETS, TIME",
author = "Roman Liesenfeld and Ingmar Nolte and Winfried Pohlmeier",
year = "2006",
month = jan,
doi = "10.1007/s00181-005-0001-1",
language = "English",
volume = "30",
pages = "795--825",
journal = "Empirical Economics",
issn = "0377-7332",
publisher = "Springer-Verlag",
number = "4",

}

RIS

TY - JOUR

T1 - Modelling financial transaction price movements

T2 - a dynamic integer count data model

AU - Liesenfeld, Roman

AU - Nolte, Ingmar

AU - Pohlmeier, Winfried

PY - 2006/1

Y1 - 2006/1

N2 - In this paper we develop a dynamic model for integer counts to capture fundamental properties of financial prices at the transaction level. Our model relies on an autoregressive multinomial component for the direction of the price change and a dynamic count data component for the size of the price changes. Since the model is capable of capturing a wide range of discrete price movements it is particularly suited for financial markets where the trading intensity is moderate or low. We present the model at work by applying it to transaction data of two shares traded at the NYSE traded over a period of one trading month. We show that the model is well suited to test some theoretical implications of the market microstructure theory on the relationship between price movements and other marks of the trading process. Based on density forecast methods modified for the case of discrete random variables we show that our model is capable to explain large parts of the observed distribution of price changes at the transaction level.

AB - In this paper we develop a dynamic model for integer counts to capture fundamental properties of financial prices at the transaction level. Our model relies on an autoregressive multinomial component for the direction of the price change and a dynamic count data component for the size of the price changes. Since the model is capable of capturing a wide range of discrete price movements it is particularly suited for financial markets where the trading intensity is moderate or low. We present the model at work by applying it to transaction data of two shares traded at the NYSE traded over a period of one trading month. We show that the model is well suited to test some theoretical implications of the market microstructure theory on the relationship between price movements and other marks of the trading process. Based on density forecast methods modified for the case of discrete random variables we show that our model is capable to explain large parts of the observed distribution of price changes at the transaction level.

KW - financial transaction prices

KW - autoregressive conditional multinomial model

KW - GLARMA

KW - count data

KW - market microstructure effects

KW - TRADING VOLUME

KW - STOCK-PRICES

KW - VOLATILITY

KW - INFORMATION

KW - CONSTRAINTS

KW - ADJUSTMENT

KW - MARKETS

KW - TIME

U2 - 10.1007/s00181-005-0001-1

DO - 10.1007/s00181-005-0001-1

M3 - Journal article

VL - 30

SP - 795

EP - 825

JO - Empirical Economics

JF - Empirical Economics

SN - 0377-7332

IS - 4

ER -