Improving Mean Variance Optimization through Sparse Hedging Restrictions
针对有限样本下多重共线性导致对冲交易不稳定的问题，提出一种稀疏逆协方差矩阵估计方法，通过收缩交易规模和减少每笔对冲中的股票数量，显著降低样本外风险并提高扣除交易成本后的确定性等价收益。
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.