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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...
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In finance, durations between successive transactions are usually modelled by the autoregressive conditional duration model based on a continuous distribution omitting frequent zero values. Zero durations can be caused by either split transactions or independent transactions. We propose a...
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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....
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