Honey, I shrunk the sample covariance matrix - Problems in mean-variance optimization.
Honey, I shrunk the sample covariance matrix - Problems in mean-variance optimization.
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DOI：
10.3905/jpm.2004.110
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发表时间：
2004-06-01
影响因子：
1.4
通讯作者：
Wolf, M
中科院分区：
文献类型：
--
作者：
Ledoit, O;Wolf, M
本文的中心信息是，没有人应该使用样本协方差矩阵进行投资组合优化。它受到估计误差的影响，这种估计误差最有可能干扰均值方差优化器。相反，可以通过称为收缩的变换从样本协方差矩阵中获得一个矩阵。这倾向于将最极端的系数拉向更中心的值，在最重要的时候系统地减少估计误差。统计上的挑战在于知道最佳收缩强度。收缩减少了相对于基准指数的投资组合跟踪误差，并大大提高了经理的实现信息比率。
yThe central message of this article is that no one should use the sample covariance matrix for portfolio optimization. It is subject to estimation error of the kind most likely to perturb a mean-variance optimizer. Instead, a matrix can be obtained from the sample covariance matrix through a transformation called shrinkage. This tends to pull the most extreme coefficients toward more central values, systematically reducing estimation error when it matters most. Statistically, the challenge is to know the optimal shrinkage intensity. Shrinkage reduces portfolio tracking error relative to a benchmark index, and substantially raises the manager's realized information ratio.