Dec 1, 2020 11:00am to 12:30pm
Happening As Scheduled
ABSTRACT: The GPS (Goldberg, Papanicolaou, Shkolnik) method shrinks the leading eigenvector of the sample covariance matrix towards the vector of all 1âs by a data driven amount in the low sample-high dimension regime. That creates an estimate of betas that has lower l_2 error and significantly reduces the impact of the estimation error on minimum variance portfolio weights and risk forecasts. We...
Hubeyb Gurdogan, Florida State University (Speaker)