Improving Mean Variance Optimization through Sparse Hedging Restrictions

By Shingo Goto, Yan Xu. Journal of Financial and Quantitative Analysis, 2015

85 citations recorded by the source corpus.

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, nonnegativity constraints), a portfolio formed on the proposed estimator achieves significant out-of-sample risk reduction and improves certainty equivalent returns after transaction costs.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/efbd8db25d8136f35053504614d596de44dccbf0

Citations in the corpus

• Dissecting Characteristics Nonparametrically (2020, unknown): “Haugen and Baker (1996) and Lewellen (2015) are notable exceptions.”