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Random matrix ensembles of time-lagged correlation matrices : derivation of eigenvalue spectra and analysis of financial time-series


Thurner, Stefan; Biely, Christoly


Bitte beziehen Sie sich beim Zitieren dieses Dokumentes immer auf folgenden Persistent Identifier (PID):http://nbn-resolving.de/urn:nbn:de:0168-ssoar-221153

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Abstract We derive the exact form of the eigenvalue spectra of correlation matrices derived from a set of time-shifted, finite Brownian random walks (time-series). These matrices can be seen as real, asymmetric random matrices where the time-shift superimposes some structure. We demonstrate that for large matrices the associated eigenvalue spectrum is circular symmetric in the complex plane. This fact allows us to exactly compute the eigenvalue density via an inverse Abel-transform of the density of the {\it symmetrized} problem. We demonstrate the validity of this approach numerically. Theoretical findings are next compared with eigenvalue densities obtained from actual high frequency (5 min) data of the S\&P500 and discuss the observed deviations. We identify various non-trivial, non-random patterns and find asymmetric dependencies associated with eigenvalues departing strongly from the Gaussian prediction in the imaginary part. For the same time-series, with the market contribution removed, we observe strong clustering of stocks, into causal sectors. We finally comment on the stability of the observed patterns.
Klassifikation Allgemeines, spezielle Theorien und "Schulen", Methoden, Entwicklung und Geschichte der Wirtschaftswissenschaften; Wirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatik
Methode Theorieanwendung
Freie Schlagwörter Stochastic analysis; Adaptive behaviour; Agent based modelling; Asset pricing; Complexity in economics; Financial time series
Sprache Dokument Englisch
Publikationsjahr 2008
Seitenangabe S. 705-722
Zeitschriftentitel Quantitative Finance, 8 (2008) 7
DOI http://dx.doi.org/10.1080/14697680701691477
Status Postprint; begutachtet (peer reviewed)
Lizenz PEER Licence Agreement (applicable only to documents from PEER project)