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We present an original and novel method based on random matrix approach thatenables to distinguish the respective role of temporal autocorrelations insidegiven time series and cross correlations between various time series. Theproposed algorithm is based on properties of Wigner eigenspectrum of randommatrices instead of commonly used Wishart eigenspectrum methodology. Theproposed approach is then qualitatively and quantitatively applied to financialdata in stocks building WIG (Warsaw Stock Exchange Index).

Identification of cross and autocorrelations in time series within an approach based on Wigner eigenspectrum of random matrices