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Wikipedia defines the Epps effect as follows:

In econometrics and time series analysis, the Epps effect, named after T. W. Epps, is the phenomenon that the empirical correlation between the returns of two different stocks decreases as the sampling frequency of data increases. The phenomenon is caused by non-synchronous/asynchronous trading and discretization effects.

Epps wrote this paper in 1979 and the speed of information propagation has increased dramatically since.

Does this effect still exist? If it does, what is the time horizon in practice when does the "decoupling" of assets happen now?

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How is he defining correlation? – user2763361 Oct 18 '13 at 10:38
up vote 4 down vote accepted

I think it's alive and well. I don't think there's a specific "decoupling" time, but if you look at e.g. Munnix et al. "Statistical causes for the Epps effect in microstructure noise", it seems that the biased correlation is about 60% of the real value for 1 min data and about 90% for 5 min data, so you could say that 5 min is pretty safe, but 1 min is probably not.

FYI, for example Zhang et al. "Estimating Covariation: Epps Effect, Microstructure Noise" present a fairly straightforward way to effectively remove the Epps effect by considering a certain difference of correlations such that the Epps effect/ bias cancels. It works if you happen to have the real tick data at hand, but if you only have, say, 1 min data from a broker etc. then I guess you pretty much have to live with the Epps effect.

I think it is still better to use 1 min data instead of e.g. 5 min data due to the increased resolution (though I'm not sure). It's just that for 1 min data the correlations could be better estimated by taking the Epps effect into account.

EDIT: By the way, the data in the Munnix paper seemed to be from 2007, so maybe things have changed since then...

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