Improving Mean Variance Optimization Through Sparse Hedging Restrictions

52 Pages Posted: 10 Nov 2013

See all articles by Shingo Goto

Shingo Goto

University of Rhode Island - College of Business Administration

Yan Xu

HKU, Faculty of Business and Economics

Date Written: October 30, 2013

Abstract

In portfolio risk minimization, the inverse covariance matrix prescribes the hedge trades in which a stock is hedged by all the other stocks in the portfolio. In practice with finite samples, however, multicollinearity makes the hedge trades too unstable and unreliable. By shrinking trade sizes and reducing the number of stocks in each hedge trade, we propose a "sparse'' estimator of the inverse covariance matrix. Comparing favorably with other methods (equal weighting, shrunk covariance matrix, industry factor model, non-negativity constraints), a portfolio formed on the proposed estimator achieves significant out-of-sample risk reduction and improves certainty equivalent returns after transaction costs.

Keywords: sparse inverse covariance matrix, hedging relationships, mean variance optimizer

JEL Classification: G11

Suggested Citation

Goto, Shingo and Xu, Yan, Improving Mean Variance Optimization Through Sparse Hedging Restrictions (October 30, 2013). Journal of Financial and Quantitative Analysis (JFQA), Forthcoming, Available at SSRN: https://ssrn.com/abstract=2351939

Shingo Goto

University of Rhode Island - College of Business Administration ( email )

Kingston, RI 02881
United States
4018744318 (Phone)
4018744312 (Fax)

Yan Xu (Contact Author)

HKU, Faculty of Business and Economics ( email )

Pok Fu Lam Road
Hong Kong
Hong Kong

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
533
Abstract Views
2,252
rank
62,404
PlumX Metrics