Seminar | October 4 | 11 a.m.-12:30 p.m. | Zoom
Alec Kercheval, Florida State University
Consortium for Data Analytics in Risk
Portfolio risk forecasts require an estimate of the covariance matrix of asset returns, often for a large number of assets. When only a small number of observations are available, we are in the high-dimension-low-sample-size (HL) regime in which estimation error dominates. Factor models are used to decrease the dimension, but the factors still need to be estimated. We describe a shrinkage estimator for the first principal component, called James-Stein for Eigenvectors (JSE), that is parallel to the famous James-Stein estimator for a collection of averages. In the context of a 1-factor model, JSE substantially improves optimization-based metrics for the minimum variance portfolio. With certain extra information, JSE is a consistent estimator of the leading eigenvector. This is based on joint work with Lisa Goldberg, Hubeyb Gurdogan, and Alex Shkolnik.
Wouter Leenders, leenders@berkeley.edu, 510-