Statistical causes for the Epps effect in microstructure noise

TL;DR

This paper identifies two statistical reasons for the Epps effect in high-frequency financial data—trade asynchrony and price decimalization—and proposes parameter-free correction methods validated on empirical data.

Contribution

It introduces novel, parameter-free statistical compensation methods for the Epps effect caused by trade asynchrony and decimalization, validated through models and empirical NYSE data.

Findings

Major fraction of the Epps effect can be compensated.

Compensation methods are especially effective for low-priced stocks.

The causes are of purely statistical origin, depending on time series properties.

Abstract

We present two statistical causes for the distortion of correlations on high-frequency financial data. We demonstrate that the asynchrony of trades as well as the decimalization of stock prices has a large impact on the decline of the correlation coefficients towards smaller return intervals (Epps effect). These distortions depend on the properties of the time series and are of purely statistical origin. We are able to present parameter-free compensation methods, which we validate in a model setup. Furthermore, the compensation methods are applied to high-frequency empirical data from the NYSE's TAQ database. A major fraction of the Epps effect can be compensated. The contribution of the presented causes is particularly high for stocks that are traded at low prices.