A new procedure for resampled portfolio with shrinkaged covariance matrix

Mian Huang and Shangbing Yu

Journal of Applied Statistics, 2020, vol. 47, issue 4, 642-652

Abstract: 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.

Date: 2020
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DOI: 10.1080/02664763.2019.1648394

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