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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
