  Sample correlation matrices are employed ubiquitously in statistics. However,
quite surprisingly, little is known about their asymptotic spectral properties
for high-dimensional data, particularly beyond the case of "null models" for
which the data is assumed independent. Here, considering the popular class of
spiked models, we apply random matrix theory to derive asymptotic first-order
and distributional results for both the leading eigenvalues and eigenvectors of
sample correlation matrices. These results are obtained under high-dimensional
settings for which the number of samples n and variables p approach infinity,
with p/n tending to a constant. To first order, the spectral properties of
sample correlation matrices are seen to coincide with those of sample
covariance matrices; however their asymptotic distributions can differ
significantly, with fluctuations of both the sample eigenvalues and
eigenvectors often being remarkably smaller than those of their sample
covariance counterparts.
