%0 Book Section %1 statphys23_0485 %A Matteo, T. Di %A Aste, T. %B Abstract Book of the XXIII IUPAP International Conference on Statistical Physics %C Genova, Italy %D 2007 %E Pietronero, Luciano %E Loreto, Vittorio %E Zapperi, Stefano %K complex econophysics networks statphys23 systems topic-11 %T How do we infer interactions from correlations? %U http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=485 %X One of the main goals in the field of complex systems is the extraction of relevant and meaningful information about the properties of the underlying system 1-3. The recovering of information about the underlying system of interactions allows us to build a ‘geometrical’ model able to take into account complex collective fluctuations 4. In this talk we discuss these issues by using cross-correlation matrices of different financial data. We show several ways to extract a structure of interactions comparing different methods including the spectra analysis 5-7. We discuss the effects of ‘noise dressing’ due to the finiteness of the time series and we present results of the fluctuations of correlations over different time- periods. 1) T. Aste, T. Di Matteo, S. T. Hyde, Physica A 346 (2005) 20-26.\\ 2) M. Tumminello, T. Aste, T. Di Matteo, R. N. Mantegna, PNAS 102, n. 30 (2005) 10421 10426.\\ 3) M. Tumminello, T. Aste, T. Di Matteo, and R. N. Mantegna, EPJB 55 (2007) 209-217.\\ 4) T. Aste and T. Di Matteo, Physica A 370 (2006) 156-161.\\ 5) L. Laloux, P. Cizeau, J.-P. Bouchaud, M. Potters, Phys. Rev. Lett. 83 (1999) 1467–1470.\\ 6) V. Plerou, P. Gopikrishnan, B. Rosenow, L.A.N. Amaral, H.E. Stanley, Phys. Rev. Lett. 83 (1999) 1471–1474.\\ 7) L. Giada, M. Marsili, Phys. Rev. E 63 (2001) 061101.

@incollection{statphys23_0485, abstract = {One of the main goals in the field of complex systems is the extraction of relevant and meaningful information about the properties of the underlying system [1-3]. The recovering of information about the underlying system of interactions allows us to build a ‘geometrical’ model able to take into account complex collective fluctuations [4]. In this talk we discuss these issues by using cross-correlation matrices of different financial data. We show several ways to extract a structure of interactions comparing different methods including the spectra analysis [5-7]. We discuss the effects of ‘noise dressing’ due to the finiteness of the time series and we present results of the fluctuations of correlations over different time- periods. 1) T. Aste, T. Di Matteo, S. T. Hyde, Physica A 346 (2005) 20-26.\\ 2) M. Tumminello, T. Aste, T. Di Matteo, R. N. Mantegna, PNAS 102, n. 30 (2005) 10421 10426.\\ 3) M. Tumminello, T. Aste, T. Di Matteo, and R. N. Mantegna, EPJB 55 (2007) 209-217.\\ 4) T. Aste and T. Di Matteo, Physica A 370 (2006) 156-161.\\ 5) L. Laloux, P. Cizeau, J.-P. Bouchaud, M. Potters, Phys. Rev. Lett. 83 (1999) 1467–1470.\\ 6) V. Plerou, P. Gopikrishnan, B. Rosenow, L.A.N. Amaral, H.E. Stanley, Phys. Rev. Lett. 83 (1999) 1471–1474.\\ 7) L. Giada, M. Marsili, Phys. Rev. E 63 (2001) 061101.}, added-at = {2007-06-20T10:16:09.000+0200}, address = {Genova, Italy}, author = {Matteo, T. Di and Aste, T.}, biburl = {https://www.bibsonomy.org/bibtex/21ae2854a377d6305c14a7e6293626157/statphys23}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Pietronero, Luciano and Loreto, Vittorio and Zapperi, Stefano}, interhash = {7d429a4f227541af15707e774fedf554}, intrahash = {1ae2854a377d6305c14a7e6293626157}, keywords = {complex econophysics networks statphys23 systems topic-11}, month = {9-13 July}, timestamp = {2007-06-20T10:16:21.000+0200}, title = {How do we infer interactions from correlations?}, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=485}, year = 2007 }