Journal of Financial & Quantitative Analysis. Volume 50, Issue 6 December 2015 , pp. 1415-1441

通过稀疏对冲限制提高均值方差优化效果

作者：Shingo Goto (University of South Carolina, Moore School of Business), Yan Xu (University of Hong Kong, Faculty of Business and Economics)

摘要：在投资组合风险最小化过程中，逆协方差矩阵要求在对冲交易中一只股票要能被投资组合中其他所有的股票对冲掉。然而实际上在有限样本条件下，多重共线性使得对冲交易很不稳定也很不可靠。通过压缩交易规模和减少对冲交易中股票的数量，我们提出了逆协方差矩阵的稀疏估计。通过与其他方式（等权重、压缩协方差矩阵，行业因子模型、非负限定）的比较，根据我们提出的估计方法构建的投资组合能显著降低样本外风险，并提高考虑交易费用后获得同等收益的确定性。

Improving Mean Variance Optimization through Sparse Hedging Restrictions

Shingo Goto (University of South Carolina, Moore School of Business)

Yan Xu (University of Hong Kong, Faculty of Business and Economics)

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.

原文链接: https://www.cambridge.org/core/journals/journal-of-financial-and-quantitative-analysis/article/improving-mean-variance-optimization-through-sparse-hedging-restrictions/032B5C00401E97C17FB249481D1095B7

翻译：汪国颂