Contributions to statistical modelling of high frequency financial data with applications to Frankfurt Stock Exchange This dissertation is concerned with the forecasting performance of time series models for the price movements of high frequency transaction data on the Frankfurt Stock Exchange. The availability of high quality data of this kind at an affordable cost makes it possible to investigate the intra daily price movements and their relationship with the corresponding trading volumes. The first of the two main chapters is concerned with models to describe price durations i.e. the time intervals between successive price changes of at least a predetermined amount. We investigate the forecasting performance of the sophisticated Markov switching autoregressive conditional duration model proposed by Hujer et al. (2002). It is demonstrated that the model performed quite poorly if strictly out of sample assessments are made. The source of this problem is identified and an explanation is provided as to why the findings in the literature suggest a more positive view of the model ...