IMF-高维协方差矩阵估计：对角目标的收缩（英）-2023.12

# 高维协 # 方差矩阵 # 均方误差大小：2.69M | 页数：32 | 上架时间：2023-12-13 | 语言：英文

IMF-高维协方差矩阵估计：对角目标的收缩（英）-2023.12.pdf

IMF-高维协方差矩阵估计：对角目标的收缩（英）-2023.12.pdf

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类型: 专题

上传者: 李琳琳

撰写机构: IMF

出版日期: 2023-12-13

摘要：

This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by

extending the Oracle Approximating Shrinkage (OAS) of Chen et al. (2009) to target the diagonal elements of

the sample covariance matrix. We derive the closed-form solution of the shrinkage parameter and show by

simulation that, when the diagonal elements of the true covariance matrix exhibit substantial variation, our

method reduces the Mean Squared Error, compared with the OAS that targets an average variance. The

improvement is larger when the true covariance matrix is sparser. Our method also reduces the Mean Squared

Error for the inverse of the covariance matrix.

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