Zusammenfassung
We analyse the dependence of stock return cross-correlations on the sampling
frequency of the data known as the Epps effect: For high resolution data the
correlations are significantly smaller than their asymptotic value as
observed on daily data. We demonstrate the deficiencies of the existing
description and give a relation between correlations on different time
scales. After testing our method on a model of generated random walk price
changes we justify our analytical results by fitting the correlation curves
of real world data. Our results indicate that the Epps phenomenon is a
product of the finite time decay of lagged correlations of high resolution
data, which does not scale with activity. The characteristic time is due to
a human time scale, the time needed to react to news.
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