Efficient mean-variance portfolio selection by double regularization

N'Golo Kone ()

No 1453, Working Paper from Economics Department, Queen's University

Abstract: This paper addresses the estimation issue that exists when estimating the traditional mean-variance portfolio. More precisely, the efficient mean-variance is estimated by a double regularization. These regularization techniques namely the ridge, the spectral cut-off, and Landweber-Fridman involve a regularization parameter or penalty term whose optimal value needs to be selected efficiently. A data-driven method has been proposed to select the tuning parameter. We show that the double regularized portfolio guarantees to investors the maximum expected return with the lowest risk. In empirical and Monte Carlo experiments, our double regularized rules are compared to several strategies, such as the traditional regularized portfolios, the new Lasso strategy of Ao et al. (2019), and the naive 1/N strategy in terms of in-sample and out-of-sample Sharpe ratio performance, and it is shown that our method yields significant Sharpe ratio improvements and a reduction in the expected utility loss.

Keywords: Portfolio selection; efficient mean-variance analysis; double regularization (search for similar items in EconPapers)
JEL-codes: C52 C58 G11 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2021-02
New Economics Papers: this item is included in nep-cwa, nep-fmk, nep-ore, nep-rmg and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1453.pdf First version 2021 (application/pdf)

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:qed:wpaper:1453

Access Statistics for this paper

More papers in Working Paper from Economics Department, Queen's University Contact information at EDIRC.
Bibliographic data for series maintained by Mark Babcock ().