Portfolio Optimization Using Forward-Looking Information
Portfolio Optimization Using Forward-Looking Information
复制标题
利用前瞻性信息优化投资组合
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
S. Sassning
中科院分区：
文献类型：
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作者：
A. Kempf;O. Korn;S. Sassning
我们发展了一个新的协方差矩阵估计族，它完全依赖于前瞻信息。它只使用当前价格的普通期权。在样本外研究中，我们表明基于这些完全隐含估计量的最小方差策略优于几种基准策略，包括基于历史估计的各种策略、指数投资和1/N投资。这种超常表现源于信息低和信息不对称程度较高的危机时期。尽管历史基准策略在使用更新的数据时会有所改善，但它们的表现永远不会超过完全隐含的策略。因此，我们的结果表明，投资者更好地依赖前瞻性信息。
We develop a new family of estimators of the covariance matrix that relies solely on forwardlooking information. It uses only current prices of plain-vanilla options. In an out-of-sample study we show that a minimum-variance strategy based on these fully-implied estimators outperforms several benchmark strategies, including various strategies based on historical estimates, index investing, and 1/N investing. The outperformance originates in crisis periods when information ow and information asymmetry are high. Although the historical benchmark strategies improve when more recent data is used, they never outperform fully-implied strategies. Thus, our results suggest that investors are better off relying on forward-looking information.