 Reduction of estimation risk in optimal portfolio choice using redundant constraintsLuis Chavez-Bedoya and Francisco Rosales International Review of Financial Analysis, 2021, vol. 78, issue C Abstract: It is well known that when the moments of the distribution governing returns are estimated from sample data, the out-of-sample performance of the optimal solution of a mean–variance (MV) portfolio problem deteriorates as a consequence of the so-called “estimation risk”. In this document we provide a theoretical analysis of the effects caused by redundant constraints on the out-of-sample performance of optimal MV portfolios. In particular, we show that the out-of-sample performance of the plug-in estimator of the optimal MV portfolio can be improved by adding any set of redundant linear constraints. We also illustrate our findings when risky assets are equally correlated and identically distributed. In this specific case, we report an emerging trade-off between diversification and estimation risk and that the allocation of estimation risk across portfolios forming the optimal solution changes dramatically in terms of number of assets and correlations. Keywords: Portfolio optimization; Orthogonal portfolios; Estimation risk; Global minimum-variance portfolio; Zero-investment portfolio; Active portfolio management (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:78:y:2021:i:c:s1057521921002532 DOI: 10.1016/j.irfa.2021.101930 Access Statistics for this article International Review of Financial Analysis is currently edited by B.M. Lucey More articles in International Review of Financial Analysis from Elsevier |
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