Optimality of Naive Investment Strategies in Dynamic Mean-Variance Optimization Problems with Multiple Priors

Kyoto University, Graduate School of Economics Discussion Paper Series No. E-16-004

46 Pages Posted: 5 Jul 2016 Last revised: 7 Jul 2016

Date Written: July 4, 2016

Abstract

We study dynamic mean-variance optimization problems with multiple priors. We introduce two types of multiple priors, the priors for expected returns and the priors for covariances. Our framework suggests that the global minimum-variance portfolio is optimal when the investor strongly doubts the correctness of the estimated expected returns, and the equally weighted portfolio is optimal when the investor strongly doubts the correctness of the estimated covariances. From the back tests, we find that for some data sets, the strategy that invests in the global minimum-variance portfolio or the equally weighted portfolio considering the market condition is more efficient than the other mean-variance efficient portfolios.

Keywords: robust mean-variance optimization, dynamic portfolio selections, naive diversification, global minimum-variance portfolio, mean-variance efficiency

JEL Classification: G11

Suggested Citation

Shigeta, Yuki, Optimality of Naive Investment Strategies in Dynamic Mean-Variance Optimization Problems with Multiple Priors (July 4, 2016). Kyoto University, Graduate School of Economics Discussion Paper Series No. E-16-004. Available at SSRN: https://ssrn.com/abstract=2804523 or http://dx.doi.org/10.2139/ssrn.2804523

Yuki Shigeta (Contact Author)

Tokyo Keizai University ( email )

1-7-34, Minami-cho, Kokubunji-shi, Tokyo
Tokyo, 185-8502
Japan

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