  Financial correlation matrices measure the unsystematic correlations between
stocks. Such information is important for risk management. The correlation
matrices are known to be ``noise dressed''. We develop a new and alternative
method to estimate this noise. To this end, we simulate certain time series and
random matrices which can model financial correlations. With our approach,
different correlation structures buried under this noise can be detected.
Moreover, we introduce a measure for the relation between noise and
correlations. Our method is based on a power mapping which efficiently
suppresses the noise. Neither further data processing nor additional input is
needed.
