Improving Minimum-Variance Portfolios by Alleviating Overdispersion of Eigenvalues
针对样本协方差矩阵特征值过度分散导致逆协方差矩阵估计误差大的问题，提出基于Schatten范数收缩特征值的通用框架，计算高效且无需特定结构，实证显示能降低样本外风险与换手率。
In portfolio risk minimization, the inverse covariance matrix of returns is often unknown and has to be estimated in practice. Yet the eigenvalues of the sample covariance matrix are often overdispersed, leading to severe estimation errors in the inverse covariance matrix. To deal with this problem, we propose a general framework by shrinking the sample eigenvalues based on the Schatten norm. The proposed framework has the advantage of being computationally efficient as well as structure-free. The comparative studies show that our approach behaves reasonably well in terms of reducing out-of-sample portfolio risk and turnover.