A general approach to smooth and convex portfolio optimization using lower partial moments

Haixiang Yao, Jinbo Huang, Yong Li and Jacquelyn E. Humphrey

Journal of Banking & Finance, 2021, vol. 129, issue C

Abstract: We propose a new nonparametric kernel (NPK) mean-lower partial moments model for portfolio construction that includes transaction costs. In the theory section, we study the properties of the solution to this model. We use simulated financial returns to demonstrate that the NPK model outperforms the traditional moment (MOM) model in terms of estimation accuracy, portfolio performance and transaction costs. We then empirically test our model using actual hedge fund returns, because holding these assets can expose investors to substantial downside risk. Portfolios formed using our NPK model significantly outperform those formed using the MOM model and other conventional investment strategies, regardless of the performance metric examined. Our NPK model will therefore be useful in a wide variety of contexts requiring downside risk management

Keywords: Portfolio optimization; Nonparametric kernel estimation; Lower partial moments; Transaction costs (search for similar items in EconPapers)
JEL-codes: C61 G11 G23 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:129:y:2021:i:c:s0378426621001266

DOI: 10.1016/j.jbankfin.2021.106167

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