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Abstract
We analyse the dependence of stock return cross-correlations on the data sampling frequency, known as the Epps effect: for high-resolution data the cross-correlations are significantly smaller than their asymptotic value as observed for daily data. The former description implies that a changing trading frequency should alter the characteristic time of the phenomenon. This is not true for empirical data: the Epps curves do not scale with market activity. The latter result indicates that the time scale of the phenomenon is related to the reaction time of market participants (this we denote as the 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.
Acknowledgements
The support of OTKA T049238 is acknowledged.
