报告人：Lisa R. Goldberg（University of California, Berkeley）
时间：2018-11-06 15:00-16:00
地点：Room 1560, Sciences Building No. 1
Abstract: Estimation error has plagued quantitative finance since Harry Markowitz launched modern
portfolio theory in 1952. Using random matrix theory, we characterize a source of bias in the sample
eigenvectors of financial covariance matrices. Unchecked, the bias distorts weights of minimum
variance portfolios and leads to risk forecasts that are severely biased downward. To address these
issues, we develop 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. We will illustrate the effectiveness of our method with numerical examples.
Working paper: //papers.ssrn.com/sol3/papers.cfm?abstract_id=3071328
