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Improving Mean Variance Optimization through Sparse Hedging Restrictions

Published online by Cambridge University Press:  18 January 2016

Shingo Goto  and
Yan Xu
Shingo Goto*
Affiliation:
shingo.goto@moore.sc.edu, University of South Carolina, Moore School of Business, Columbia, SC 29208
Yan Xu
Affiliation:
yanxuj@hku.hk, University of Hong Kong, Faculty of Business and Economics, Hong Kong.
*
*Corresponding author: shingo.goto@moore.sc.edu
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Abstract

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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.

Information

Type
Research Articles
Information
Journal of Financial and Quantitative Analysis , Volume 50 , Issue 6 , December 2015 , pp. 1415 - 1441
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2016 

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