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The paper tackles the problem of deriving a topological structure among stockprices from high frequency historical values. Similar studies using lowfrequency data have already provided valuable insights. However, in those casesdata need to be collected for a longer period and then they have to bedetrended. An effective technique based on averaging a metric function on shortsubperiods of the observation horizon is suggested. Since a standardcorrelation-based metric is not capable of catching dependencies at differenttime instants, it is not expected to perform the best when dealing with highfrequency data. Hence, the choice of a more suitable metric is discussed. Inparticular, a coherence-based metric is proposed, for it is able to detect anypossible linear relation between two times series, even at different timeinstants. The averaging technique is employed to analyze a set of 100 highvolume stocks of the New York Stock Exchange, observed during March 2008.

Coherence-based multivariate analysis of high frequency stock market values