Correcting Bias in Eigenvectors of Financial Covariance Matrices

Neyman Seminar
Sep 19, 2018 4:00pm to 5:00pm
1011 Evans Hall
Happening As Scheduled
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...
Alex Papanicolaou, UC Berkeley