Some new efficient mean–variance portfolio selection models
针对均值-方差模型样本外表现差的问题，提出三种改进：用L1正则化得到稀疏组合、用Ledoit-Wolf收缩法估计协方差矩阵、用鲁棒优化减少预期收益估计误差，实证显示新策略表现更好。
Abstract The poor out‐of‐sample performance of mean–variance portfolio model is mainly caused by estimation errors in the covariance matrix and the mean return, especially the mean return vector. Meanwhile, in financial practice, what most investors actually like is to hold a few stocks in their portfolio. The goal of this paper is to propose some new efficient mean–variance portfolio selection models by considering the following aspects: (i) use the L 1 ‐regularization in objective function to obtain sparse portfolio; (ii) use the shrinkage method of Ledoit and Wolf, Journal of Economics Financial, 2003, 10, 603–621 to estimate the covariance matrix; (iii) use the robust optimization method to mitigate the estimation errors of the expected return. Finally, empirical analysis demonstrates that the proposed strategies have better out‐of‐sample performance.