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APS » Journals » Phys. Rev. E » Volume 75 » Issue 3
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Phys. Rev. E 75, 036110 (2007) [7 pages]

Weighted network analysis of high-frequency cross-correlation measures

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Giulia Iori*
Department of Economics, City University, Northampton Square, London, EC1V 0HB, United Kingdom

Ovidiu V. Precup†
Department of Mathematics, London School of Economics, Houghton Street, London, WC2A 2AE, United Kingdom

Received 25 October 2006; revised 19 December 2006; published 26 March 2007

In this paper we implement a Fourier method to estimate high-frequency correlation matrices from small data sets. The Fourier estimates are shown to be considerably less noisy than the standard Pearson correlation measures and thus capable of detecting subtle changes in correlation matrices with just a month of data. The evolution of correlation at different time scales is analyzed from the full correlation matrix and its minimum spanning tree representation. The analysis is performed by implementing measures from the theory of random weighted networks.

© 2007 The American Physical Society

URL:
http://link.aps.org/doi/10.1103/PhysRevE.75.036110
DOI:
10.1103/PhysRevE.75.036110
PACS:
89.65.Gh

*Corresponding author. Email address: g.iori@city.ac.uk

†Email address: O.V.Precup@lse.ac.uk

 
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