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A new procedure for resampled portfolio with shrinkaged covariance matrix

机译:重采样投资组合的新程序,具有收缩协方差矩阵

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摘要

Dealing with estimation error is an important issue when we implement the mean-variance paradigm for portfolio construction. To tackle the problem, two approaches are proposed in literature, the portfolio resampling technique introduced by Michuad and the well-known shrinkaged covariance matrix method. There are certain evidences on the advantages of shrinkaged covariance over portfolio resampling, however, it is unclear whether a combination of the two approaches could produce a better performance compared with using shrinkaged covariance alone. In this paper, we propose a new algorithm to integrated linear or nonlinear shrinkage estimation with resampled portfolio to achieve a further improvement. Our method are demonstrated via extensive simulation and application in active portfolio management process.
机译:处理估计错误是我们实施投资组合建设的平均方差范例时的重要问题。为了解决问题,文献中提出了两种方法,由Michuad引入的产品组合重采样技术和众所周知的收缩协方差矩阵方法。在投资组合重新采样中,对协方差收缩的优势有一定可以证明,目前尚不清楚两种方法的组合可以单独使用收缩协方差而产生更好的性能。在本文中,我们提出了一种新的算法与重采样的组合进行了集成的线性或非线性收缩估计,以实现进一步的改进。我们的方法通过广泛的仿真和应用在活动组合管理过程中进行了演示。

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