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.

A method for detecting complex correlation in time series / V., Alfi; A., Petri; Pietronero, Luciano. - 6601:(2007), pp. U103-U109. (Intervento presentato al convegno Conference on Noise and Stochastics in Complex Systems and Finance tenutosi a Florence; Italy nel MAY 21-24, 2007) [10.1117/12.725330].

A method for detecting complex correlation in time series

PIETRONERO, Luciano
2007

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.
2007
Conference on Noise and Stochastics in Complex Systems and Finance
complex systems; financial time series; social and economic systems; statistical analysis
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A method for detecting complex correlation in time series / V., Alfi; A., Petri; Pietronero, Luciano. - 6601:(2007), pp. U103-U109. (Intervento presentato al convegno Conference on Noise and Stochastics in Complex Systems and Finance tenutosi a Florence; Italy nel MAY 21-24, 2007) [10.1117/12.725330].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/412649
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