Compensating asynchrony effects in the calculation of financial correlations

Abstract

We present a method to compensate statistical errors in the calculation of correlations on asynchronous time series. The method is based on the assumption of an underlying time series. We set up a model and apply it to financial data to examine the decrease of calculated correlations towards smaller return intervals (Epps effect). We show that this statistical effect is a major cause of the Epps effect. Hence, we are able to quantify and to compensate it using only trading prices and trading times.

13 pages, 7 figures

References in corpus (3)

• Correlation based networks of equity returns sampled at different time horizons
• The Epps effect revisited
• Credit risk - A structural model with jumps and correlations

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