  We study some properties of eigenvalue spectra of financial correlation
matrices. In particular, we investigate the nature of the large eigenvalue
bulks which are observed empirically, and which have often been regarded as a
consequence of the supposedly large amount of noise contained in financial
data. We challenge this common knowledge by acting on the empirical correlation
matrices of two data sets with a filtering procedure which highlights some of
the cluster structure they contain, and we analyze the consequences of such
filtering on eigenvalue spectra. We show that empirically observed eigenvalue
bulks emerge as superpositions of smaller structures, which in turn emerge as a
consequence of cross-correlations between stocks. We interpret and corroborate
these findings in terms of factor models, and and we compare empirical spectra
to those predicted by Random Matrix Theory for such models.

Statistical Finance