Research output: Contribution to Journal/Magazine › Journal article › peer-review
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 Journal/Magazine › Journal article › peer-review
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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 -