Optimality of Naive Investment Strategies in Dynamic MeanVariance Optimization Problems with Multiple Priors

Yuki Shigeta

Discussion papers from Graduate School of Economics , Kyoto University

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 minimumvariance 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. (search for similar items in EconPapers)
JEL-codes: G11 (search for similar items in EconPapers)
Pages: 52
Date: 2016-07
New Economics Papers: this item is included in nep-ger and nep-mic
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:kue:epaper:e-16-004

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