Partial index tracking enhanced mean–variance portfolio
提出一种部分指数追踪策略，通过将投资组合方差向追踪误差收缩来减少估计误差，实证表明该策略在稳健性和样本外追踪误差方面均优于传统方法。
Abstract Estimation constitutes a major challenge in the implementation of mean–variance portfolios. To overcome this, we propose a partial index‐tracking strategy that aims to mitigate estimation error ex‐ante. Theoretically, we minimize the mean‐squared error of the proposed strategy by shrinking the portfolio variance to its tracking error. Using an empirical design with over 50 years of data, our paper makes two important observations. First, we show that our proposed approach is consistent with both linear and non‐linear shrinkage strategies in terms of robustness. Second, the proposed decision rule leads to a lower out‐of‐sample tracking error. Our findings, overall, stress the appeal of partial index tracking not only in terms of shrinkage (robustness) but also in terms of relative performance.