Addressing estimation errors on expected asset returns through robust portfolio optimization

Gérard Cornuéjols, Özgün Elçİ and Vrishabh Patil

Quantitative Finance, 2026, vol. 26, issue 1, 85-98

Abstract: It is well known that the performance of the classical Markowitz model for portfolio optimization is extremely sensitive to estimation errors on the expected asset returns. Robust optimization mitigates this issue. We focus on ellipsoidal uncertainty sets around a point estimate of the expected asset returns. An important issue is the choice of the parameters that specify this ellipsoid, namely the point estimate and the estimation-error matrix. We show that there exist diagonal estimation-error matrices that achieve an arbitrarily small loss in the expected portfolio return as compared to the optimum. We empirically investigate the sample size needed to compute the point estimate. We also conduct an empirical study of different estimation-error matrices and give a heuristic to choose the size of the uncertainty set. The results of our experiments show that robust portfolio models featuring a family of diagonal estimation-error matrices outperform benchmark portfolio models including the classical Markowitz model.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2025.2606114 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:26:y:2026:i:1:p:85-98

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2025.2606114

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().