  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
cross-correlations are significantly smaller than their asymptotic value as
observed on daily data. The former description implies that changing trading
frequency should alter the characteristic time of the phenomenon. This is not
true for the empirical data: The Epps curves do not scale with market activity.
The latter result indicates that the time scale of the phenomenon is connected
to the reaction time of market participants (this we denote as human time
scale), independent of market activity. In this paper we give a new description
of the Epps effect through the decomposition of cross-correlations. After
testing our method on a model of generated random walk price changes we justify
our analytical results by fitting the Epps curves of real world data.
