针对大规模资产组合的 Shocks-adaptive Robust Minimum Variance Portfolio

本文提出了一种 robust、shocks-adaptive 的 portfolio，适用于 large-dimensional assets universe，where the number of assets could be comparable to or even larger than the sample size. It is well documented that portfolios based on optimizations are sensitive to outliers in return data. 我们通过提出 robust factor model 来 deal with outliers，通过 developing robust principal component analysis (PCA) for factor model estimation and a shrinkage estimation for the random error covariance matrix 来实现。这种方法扩展了 well-regarded Principal Orthogonal Complement Thresholding (POET) 方法 (Fan et al., 2013)，enabling it to effectively handle heavy tails and sudden shocks in data。proposed robust method 的 novelty 在于其 adaptiveness to both global 和 idiosyncratic shocks，without the need to distinguish them，这在 facing outliers 时对 forming portfolio weights 有用。我们 develop of the robust factor model and the robust minimum variance portfolio 的 theoretical results。Numerical and empirical results show the superior performance of the new portfolio。