Oops! I Shrunk the Sample Covariance Matrix Again: Blockbuster Meets Shrinkage
提出一种名为Blockbuster的收缩估计方法，将资产收益协方差矩阵按类别聚类，即使类别未知或随时间变化也能自适应处理，在历史数据上优于多种现有方法，有助于更高效的投资组合选择和异常检测。
Abstract Existing shrinkage techniques struggle to model the covariance matrix of asset returns in the presence of multiple-asset classes. Therefore, we introduce a Blockbuster shrinkage estimator that clusters the covariance matrix accordingly. Besides the definition and derivation of a new asymptotically optimal linear shrinkage estimator, we propose an adaptive Blockbuster algorithm that clusters the covariance matrix even if the (number of) asset classes are unknown and change over time. It displays superior all-around performance on historical data against a variety of state-of-the-art linear shrinkage competitors. Additionally, we find that for small- and medium-sized investment universes the proposed estimator outperforms even recent nonlinear shrinkage techniques. Hence, this new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of asset returns. Furthermore, due to the general structure of the proposed Blockbuster shrinkage estimator, the application is not restricted to financial problems.