Portfolio optimisation via strategy-specific eigenvector shrinkage
提出一种针对投资组合优化约束定制的协方差矩阵估计方法，通过广义James-Stein特征向量收缩减少估计误差，降低优化组合的过度波动，并给出渐近改进公式。
Abstract We estimate covariance matrices that are tailored to portfolio optimisation constraints. We rely on a generalised version of James–Stein for eigenvectors (JSE), a data-driven operator that reduces estimation error in the leading sample eigenvector by shrinking towards a target subspace determined by constraint gradients. Unchecked, this error gives rise to excess volatility for optimised portfolios. Our results include a formula for the asymptotic improvement of JSE over the leading sample eigenvector as an estimate of ground truth, and provide improved optimal portfolio estimates when variance is to be minimised subject to finitely many linear constraints.