Weighted Network Analysis of High Frequency Cross-Correlation Measures

Giulia Iori () and Ovidiu V. Precup

No 06/10, City University Economics Discussion Papers from Department of Economics, City University, London

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 measure 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 analysed 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.

Keywords: High Frequency Correlation; Fourier Method; Random weighted Networks (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
Date: 2006-11
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Forthcoming in Physical Review E, 2007

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