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Cluster analysis of high-dimensional high-frequency financial time series

Recently the availability of tick data is driving renewed interest in statistical tools for the analysis of high-dimensional irregularly spaced time series. Since the standard tools require that the data are evenly spaced, the traditional multivariate time series analysis techniques are inadequate for the analysis of tick data. We develop for perhaps the first time a proper procedure that performs cluster analysis of tick data using the joint information of the temporal process and the continuous-valued data at the actual sampling times. A simulation example studies the problem with the standard approach and demonstrates the reliability of our proposed method. Data analyses of major stock market indices and currencies are provided.