TIME SERIES MODELLING OF HIGH FREQUENCY STOCK TRANSACTION DATA

This thesis comprises four papers concerning modelling of financial count data. Paper [1], [2] and [3] advance the integer-valued moving average model (INMA), a special case of integer-valued autoregressive moving average (INARMA) model class, and apply the models to the number of stock transactions in intra-day data. Paper [4] focuses on modelling the long memory property of time series of count data and on applying the model in a financial setting. Paper [1] advances the INMA model to model the number of transactions in stocks in intra-day data. The conditional mean and variance properties are discussed and model extensions to include, e.g., explanatory variables are offered. Least squares and generalized method of moment estimators are presented. In a small Monte Carlo study a feasible least squares estimator comes out as the best choice. Empirically we find support for the use of long-lag moving average models in a Swedish stock series. There is evidence of asymmetric effects of news about prices on the number of transactions. Paper [2] introduces a bivariate integer-valued moving average (BINMA) model an