Next Article in Journal
Towards a New Paradigm for Statistical Evidence in the Use of p-Value
Previous Article in Journal
Direct and Indirect Effects under Sample Selection and Outcome Attrition
Previous Article in Special Issue
Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data
Article

Regularized Maximum Diversification Investment Strategy

Department of Economics, Queen’s University, 94 University Avenue Kingston, Kingston, ON K7L 3N6, Canada
I am greatly indebted to Marine Carrasco for her invaluable guidance. I thank Georges Dionne, Tony S. Wirjanto, Jade Wei, and two anonymous referees for their helpful comments.
Received: 17 August 2020 / Revised: 15 December 2020 / Accepted: 22 December 2020 / Published: 29 December 2020
The maximum diversification has been shown in the literature to depend on the vector of asset volatilities and the inverse of the covariance matrix of the asset return. In practice, these two quantities need to be replaced by their sample statistics. The estimation error associated with the use of these sample statistics may be amplified due to (near) singularity of the covariance matrix, in financial markets with many assets. This, in turn, may lead to the selection of portfolios that are far from the optimal regarding standard portfolio performance measures of the financial market. To address this problem, we investigate three regularization techniques, including the ridge, the spectral cut-off, and the Landweber–Fridman approaches in order to stabilize the inverse of the covariance matrix. These regularization schemes involve a tuning parameter that needs to be chosen. In light of this fact, we propose a data-driven method for selecting the tuning parameter. We show that the selected portfolio by regularization is asymptotically efficient with respect to the diversification ratio. In empirical and Monte Carlo experiments, the resulting regularized rules are compared to several strategies, such as the most diversified portfolio, the target portfolio, the global minimum variance portfolio, 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. View Full-Text
Keywords: portfolio selection; maximum diversification; regularization portfolio selection; maximum diversification; regularization
Show Figures

Figure 1

MDPI and ACS Style

Koné, N. Regularized Maximum Diversification Investment Strategy. Econometrics 2021, 9, 1. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010001

AMA Style

Koné N. Regularized Maximum Diversification Investment Strategy. Econometrics. 2021; 9(1):1. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010001

Chicago/Turabian Style

Koné, N’Golo. 2021. "Regularized Maximum Diversification Investment Strategy" Econometrics 9, no. 1: 1. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010001

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop