  We present an original and novel method based on random matrix approach that
enables to distinguish the respective role of temporal autocorrelations inside
given time series and cross correlations between various time series. The
proposed algorithm is based on properties of Wigner eigenspectrum of random
matrices instead of commonly used Wishart eigenspectrum methodology. The
proposed approach is then qualitatively and quantitatively applied to financial
data in stocks building WIG (Warsaw Stock Exchange Index).
