Weighted network analysis of high-frequency cross-correlation measures

Giulia Iori and Ovidiu V. Precup
Phys. Rev. E 75, 036110 – Published 26 March 2007

Abstract

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.

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  • Received 25 October 2006

DOI:https://doi.org/10.1103/PhysRevE.75.036110

©2007 American Physical Society

Authors & Affiliations

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

  • *Corresponding author. Email address: g.iori@city.ac.uk
  • Email address: O.V.Precup@lse.ac.uk

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Issue

Vol. 75, Iss. 3 — March 2007

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