A Comparison of High-Frequency Cross-Correlation Measures

TL;DR

This paper compares traditional interpolation-based correlation measures with a direct method for high-frequency, non-synchronous financial time series, highlighting differences in effectiveness and applicability.

Contribution

It provides a comparative analysis of interpolation-based and direct correlation measures for high-frequency data, offering insights into their relative performance.

Findings

Interpolation methods may introduce biases in correlation estimates.

Direct methods can handle raw data without homogenization.

The paper identifies scenarios where each method performs best.

Abstract

On a high-frequency scale the time series are not homogeneous, therefore standard correlation measures can not be directly applied to the raw data. There are two ways to deal with this problem. The time series can be homogenised through an interpolation method \cite{Dacorogna} (linear or previous tick) and then the Pearson correlation statistic computed. Recently, methods that can handle raw non-synchronous time series have been developed \cite{Reno1,deJong}. This paper compares two traditional methods that use interpolation with an alternative method applied directly to the actual time series.