Searching for histogram patterns due to macroscopic fluctuations in financial time series

This study aims to investigate whether the phenomena found by Shnoll et al. when applying histogrampattern analysis techniques to stochastic processes from chemistry and physics are also present infinancial time series, particularly exchange rate and index data. The phenomena are related to finestructure of non-smoothed frequency distributions drawn from statistically insufficient samples ofchanges and their patterns in time. Shnoll et al. use the notion of macroscopic fluctuations to explainthe behaviour of sequences of histograms. Histogram patterns in time adhere to several laws that couldnot be detected when using time series analysis methods.In this study general approaches are reviewed that may be used to model financial markets andthe volatility of price processes in particular. Special emphasis is placed on the modelling of highfrequencydata sets and exchange rate data. Following previous studies of the Shnoll phenomena fromother fields, different steps of the histogram sequence analysis are carried out to determine whetherthe findings of Shnoll et al. could also be applied to financial market data.The findings of this thesis widen the understanding of time varying volatility and can aid in financialrisk measurement and management. Outcomes of the study include an investigation of time seriescharacteristics in terms of the formation of discrete states, the detection of the near zone effect asproclaimed by Shnoll et al., the periodic recurrence of histogram shapes as well as the synchronousvariation in data sets measured in the same time intervals.