Jackknife Estimator for Tracking Error Variance of Optimal Portfolios
提出一种刀切估计量，用于估计基于估计协方差构建的最小跟踪误差方差投资组合的条件方差。使用200只股票的最优组合与S&P 500基准进行实证，发现该估计量比传统方法更精确且样本内乐观偏差更小。
We develop a jackknife estimator for the conditional variance of a minimum tracking error variance portfolio constructed using estimated covariances. We empirically evaluate the performance of our estimator using an optimal portfolio of 200 stocks that has the lowest tracking error with respect to the S&P 500 benchmark when three years of daily return data are used for estimating covariances. We find that our jackknife estimator provides more precise estimates and suffers less from in-sample optimism when compared to conventional estimators.