Correcting Bias in Eigenvectors of Financial Covariance Matrices
Seminar: Neyman Seminar | September 19 | 4-5 p.m. | 1011 Evans Hall
Alex Papanicolaou, UC Berkeley
There is a source of bias in the sample eigenvectors of financial covariance matrices, when unchecked, distorts weights of minimum variance portfolios and leads to risk forecasts that are severely biased downward. Recent work with Lisa Goldberg and Alex Shkolnik develops an eigenvector bias correction. Our approach is distinct from the regularization and eigenvalue shrinkage methods found in the literature. We provide theoretical guarantees on the improvement our correction provides as well as estimation methods for computing the optimal correction from data.