Improving Mean Variance Optimization Through Sparse Hedging Restrictions
52 Pages Posted: 10 Nov 2013
Date Written: October 30, 2013
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: Suggested Citation