Cross-Correlation Measures in the High-Frequency Domain

Ovidiu Precup () and Giulia Iori ()
Additional contact information
Ovidiu Precup: King’s College London

No 05/04, City University Economics Discussion Papers from Department of Economics, City University, London

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 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
Date: 2005-10
View list of references View citations in EconPapers

Forthcoming in European Journal of Finance, 2007.

Downloads: (external link)
http://www.city.ac.uk/economics/dps/discussion_papers/0504.pdf (application/pdf)

Related works:
Journal Article: Cross-correlation Measures in the High-frequency Domain (2007)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:cty:dpaper:0504

Access Statistics for this paper

More papers in City University Economics Discussion Papers from Department of Economics, City University, London
Contact information at EDIRC.
Series data maintained by Michael Ben-Gad ().