Weight bound constraints in mean-variance models: a robust control theory foundation via machine learning

Gilles Boevi Koumou

Quantitative Finance, 2024, vol. 24, issue 6, 719-733

Abstract: Using an innovative representation of the weight bound constrained Markowitz's (Portfolio selection. J. Finance, 1952, 7, 77–91) mean-variance model, developed using the support vector data description, a machine learning algorithm introduced by Tax and Duin (Support vector data description. Mach. Learn., 2004, 54, 45–66), we provide an innovative interpretation of the robustness of these bound constraints in terms of robust control theory in the sense of Hansen and Sargent (Robust control and model uncertainty. Am. Econ. Rev., 2001, 91, 60–66). Building on these insights, firstly, we detail the method for quantifying the degree of misspecification in Markowitz's (1952) mean-variance model using its counterpart with weight upper bounds. Additionally, we show that this degree of misspecification is a decreasing piecewise linear function of the bound. Secondly, we empirically investigate two simulation-based methods, inspired by Michaud's (The Markowitz optimization enigma: Is ‘optimized’ optimal? Financ. Anal. J., 1989, 45, 31–42) resampling technique, for choosing the bound. Thirdly, we compare the robustness of the weight upper bound constrained mean-variance model with that of Goldfarb and Iyengar's (Robust portfolio selection problems. Math. Oper. Res., 2003, 28, 1–38) robust maximum return model.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2024.2358954 (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:24:y:2024:i:6:p:719-733

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

DOI: 10.1080/14697688.2024.2358954

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 ().