We propose a local adaptive multiplicative error model (MEM) accommodating timevarying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time....

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...