Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 16, 2021 erstellt
Description:
Existing studies on covariance estimation generally assume that the future covariance matrix must be estimated based only on the limited history of a given set of portfolio constituents. In this study, we propose a new perspective on how to estimate the covariance matrix. We present a purely data-driven approach that selects the estimation parameters using cross validation to be historically optimal on a disjoint transfer set of assets according to the given objective. The proposed approach additionally uses a second shrinkage target that is determined based on how much the sample eigenvalues are imbalanced according to their Gini coefficient. Our empirical evaluation based on a total of six stock market indices shows that the proposed approach outperforms established estimators in minimizing variance and maximizing risk-adjusted return. The second shrinkage target is particularly relevant for high-dimensional covariance matrices where the number of assets is greater than the number of historic datapoints. To the best of our knowledge, this study is the first to apply the concept of transfer learning to the problem of covariance estimation