Cross-Correlation Measures in the High-Frequency Domain

19 Pages Posted: 9 Nov 2005

See all articles by Ovidiu Precup

Ovidiu Precup

Kings College, London - Department of Mathematics

Giulia Iori

City University London - Department of Economics

Date Written: October 28, 2005

Abstract

On a high-frequency scale, financial time series are not homogeneous, therefore standard correlation measures can not be directly applied to the raw data. To deal with this problem the time series have to be either homogenized through interpolation or methods that can handle raw non-synchronous time series need to be employed. This paper compares two traditional methods that use interpolation with an alternative method applied directly to the actual time series. The three methods are tested on simulated data and actual trades time series. The temporal evolution of the correlation matrix is revealed through the analysis of the full correlation matrix and of the Minimum Spanning Tree representation. To perform the analysis we implement several measures from the theory of random weighted networks.

Keywords: High-Frequency Correlation, Fourier method, Epps Effect, Minimum Spanning Tree, random networks

Suggested Citation

Precup, Ovidiu and Iori, Giulia, Cross-Correlation Measures in the High-Frequency Domain (October 28, 2005). Available at SSRN: https://ssrn.com/abstract=841605 or http://dx.doi.org/10.2139/ssrn.841605

Ovidiu Precup

Kings College, London - Department of Mathematics ( email )

Strand
London, England WC2R 2LS
United Kingdom

Giulia Iori (Contact Author)

City University London - Department of Economics ( email )

Northampton Square
London, EC1V 0HB
United Kingdom