A Dynamic Integer Count Data Model for Financial Transaction Prices

Abstract

In this paper we develop a dynamic model for integer counts to capture the discreteness of price changes for financial transaction prices. Our model rests 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 as for most European exchanges. We present the model at work by applying it to transaction data of the Henkel share traded at the Frankfurt stock exchange over a period of 6 months. In particular, we use the model to test some theoretical implications of the market microstructure theory on the relationship between price movements and other marks of the trading process.Autoregressive conditional multinomial model, GLARMA, transaction prices, count data, market microstructure