  We introduce a novel covariance estimator for portfolio selection that adapts
to the non-stationary or persistent heteroskedastic environments of financial
time series by employing exponentially weighted averages and nonlinearly
shrinking the sample eigenvalues through cross-validation. Our estimator is
structure agnostic, transparent, and computationally feasible in large
dimensions. By correcting the biases in the sample eigenvalues and aligning our
estimator to more recent risk, we demonstrate that our estimator performs well
in large dimensions against existing state-of-the-art static and dynamic
covariance shrinkage estimators through simulations and with an empirical
application in active portfolio management.
