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Improving Mean Variance Optimization through Sparse Hedging Restrictions
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DOI：10.1017/S0022109015000526.png)
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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.
Keyword:
COVARIANCE-MATRIX
PORTFOLIO OPTIMIZATION
NAIVE DIVERSIFICATION
PARAMETER UNCERTAINTY
EFFICIENT PORTFOLIOS
SELECTION
MODEL
LASSO
PERFORMANCE
RISK
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2.3K
被引数:
1.0W
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