Estimation of Large Financial Covariances: A Cross-Validation Approach
This work provides an improved method for estimating financial covariances, which is crucial for practitioners in active portfolio management, especially in non-stationary environments.
This paper introduces a new covariance estimator for portfolio selection that uses exponentially weighted averages and cross-validation to nonlinearly shrink sample eigenvalues. The estimator performs well in large dimensions against existing state-of-the-art static and dynamic covariance shrinkage estimators in simulations and an empirical application.
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.