  We study the spectral density of factor models of multivariate time series.
By making use of the Random Matrix Theory we analytically quantify the effect
of noise dressing on the spectral density due to the finiteness of the sample.
We consider a broad range of models ranging from one factor models in time and
frequency domain to hierarchical multifactor models.
