论文标题
大型金融协方差的估计：一种交叉验证方法
Estimation of Large Financial Covariances: A Cross-Validation Approach
论文作者
论文摘要
我们引入了一种新型的协方差估计器，以适应财务时间序列的非平稳或持续性异质性环境，通过使用指数加权平均并通过跨价加量来缩小样品特征值。我们的估计器在较大的维度上是结构不可知，透明和计算可行的。通过纠正样品特征值中的偏差，并使估计器与最近的风险保持一致，我们证明了我们的估计器在较大的维度上表现良好，以通过模拟和通过模拟和在主动投资组合管理中的经验应用来对抗现有的最新静态和动态协方差估计器。
We introduce a novel covariance estimator for portfolio selection that adapts to the non-stationary or persistent heteroskedastic environments of financial time series by employing exponentially weighted averages and nonlinearly shrinking the sample eigenvalues through cross-validation. Our estimator is structure agnostic, transparent, and computationally feasible in large dimensions. By correcting the biases in the sample eigenvalues and aligning our estimator to more recent risk, we demonstrate that our estimator performs well in large dimensions against existing state-of-the-art static and dynamic covariance shrinkage estimators through simulations and with an empirical application in active portfolio management.
