  In order to pursue the issue of the relation between the financial
cross-correlations and the conventional Random Matrix Theory we analyse several
characteristics of the stock market correlation matrices like the distribution
of eigenvalues, the cross-correlations among signs of the returns, the
volatility cross-correlations, and the multifractal characteristics of the
principal values. The results indicate that the stock market dynamics is not
simply decomposable into 'market', 'sectors', and the Wishart random bulk. This
clearly is seen when the time series used to construct the correlation matrices
are sufficiently long and thus the measurement noise suppressed. Instead, a
hierarchically convoluted and highly nonlinear organization of the market
emerges and indicates that the relevant information about the whole market is
encoded already in its constituents.
