Faculty of Business Administration
Visiting Scholar Seminar
Improving Minimum Variance Portfolios by Alleviating Over-dispersion of Eigenvalues
Dr. Fangquan SHI
Date: 16 December 2020 (Wednesday)
In portfolio risk minimization, the inverse covariance matrix of returns is often unknown and has to be estimated in practice. Yet the eigenvalues of the sample covariance matrix are often over-dispersed, leading to severe estimation errors in the inverse covariance matrix. To deal with this problem, we propose a general framework by shrinking the sample eigenvalues based on Schatten norm. The proposed framework has the advantage to be computationally efficient as well as structure free. The comparative studies show that our approach behaves reasonably well in terms of reducing out-of-sample portfolio risk and turnover.
Dr. Fangquan Shi received his Bachelor degree in Computer Science and Technology in 2012 and Master degree in Financial Engineering in 2015 respectively, both from Southwestern University of Finance and Economics. He received his PhD in Decision Science from the Faculty of Business Administration at University of Macau in 2020. He won the Macao Scientific and Technological R&D Award for Postgraduates in 2020. He has two papers published, including one on Journal of Financial and Quantitative Analysis and one on Quantitative Analysis.