Adaptive Beta Shrinkage estimation of covariance matrices

Abstract

Accurate estimation of the covariance matrix is essential for mean-variance portfolio optimisation, yet the sample covariance matrix is a notoriously noisy estimate, especially in high dimensions. Contemporary shrinkage methods attempt to mitigate this noise but often retain significant estimation error in higher-dimensional settings or become computationally impractical in these scenarios.