Making Markowitz's Portfolio Optimization Theory Practically Useful

Zhidong Bai, Huixia Liu and Wing-Keung Wong

MPRA Paper from University Library of Munich, Germany

Abstract: The traditional estimated return for the Markowitz mean-variance optimization has been demonstrated to seriously depart from its theoretic optimal return. We prove that this phenomenon is natural and the estimated optimal return is always $\sqrt{\gamma}$ times larger than its theoretic counterpart where $\gamma = \frac 1{1-y}$ with $y$ as the ratio of the dimension to sample size. Thereafter, we develop new bootstrap-corrected estimations for the optimal return and its asset allocation and prove that these bootstrap-corrected estimates are proportionally consistent with their theoretic counterparts. Our theoretical results are further confirmed by our simulations, which show that the essence of the portfolio analysis problem could be adequately captured by our proposed approach. This greatly enhances the practical uses of the Markowitz mean-variance optimization procedure.

Keywords: Optimal Portfolio Allocation, Mean-Variance Optimization; Large Random Matrix; Bootstrap Method (search for similar items in EconPapers)
JEL-codes: C13 G11 (search for similar items in EconPapers)
Date: 2016-10-08
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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