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Proceedings Paper

A method for detecting complex correlation in time series
Author(s): V. Alfi; A. Petri; L. Pietronero
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Paper Abstract

We propose a new method for detecting complex correlations in time series of limited size. The method is derived by the Spitzer's identity and proves to work successfully on different model processes, including the ARCH process, in which pairs of variables are uncorrelated, but the three point correlation function is non zero. The application to financial data allows to discriminate among dependent and independent stock price returns where standard statistical analysis fails.

Paper Details

Date Published: 15 June 2007
PDF: 7 pages
Proc. SPIE 6601, Noise and Stochastics in Complex Systems and Finance, 66010H (15 June 2007); doi: 10.1117/12.725330
Show Author Affiliations
V. Alfi, Centro Studi e Ricerche E. Fermi (Italy)
Univ. of Rome La Sapienza (Italy)
A. Petri, ISC-CNR (Italy)
L. Pietronero, Univ. of Rome La Sapienza (Italy)
ISC-CNR (Italy)


Published in SPIE Proceedings Vol. 6601:
Noise and Stochastics in Complex Systems and Finance
János Kertész; Stefan Bornholdt; Rosario N. Mantegna, Editor(s)

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