TY - JOUR

T1 - Portfolio Optimization by a Bivariate Functional of the Mean and Variance

AU - Landsman, Z.

AU - Makov, U.

AU - Shushi, T.

N1 - Funding Information:
This research was supported by the Israel Science Foundation (Grant N 1686/17). The authors are grateful to the anonymous reviewer and the Editor-in-Chief for their useful comments.
Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.

PY - 2020/5/1

Y1 - 2020/5/1

N2 - We consider the problem of maximization of functional of expected portfolio return and variance portfolio return in its most general form and present an explicit closed-form solution of the optimal portfolio selection. This problem is closely related to expected utility maximization and two-moment decision models. We show that most known risk measures, such as mean–variance, expected shortfall, Sharpe ratio, generalized Sharpe ratio and the recently introduced tail mean variance, are special cases of this functional. The new results essentially generalize previous results by the authors concerning the maximization of combination of expected portfolio return and a function of the variance of portfolio return. Our general mean–variance functional is not restricted to a concave function with a single optimal solution. Thus, we also provide optimal solutions to a fractional programming problem, that is arising in portfolio theory. The obtained analytic solution of the optimization problem allows us to conclude that all the optimization problems corresponding to the general functional have efficient frontiers belonged to the efficient frontier obtained for the mean–variance portfolio.

AB - We consider the problem of maximization of functional of expected portfolio return and variance portfolio return in its most general form and present an explicit closed-form solution of the optimal portfolio selection. This problem is closely related to expected utility maximization and two-moment decision models. We show that most known risk measures, such as mean–variance, expected shortfall, Sharpe ratio, generalized Sharpe ratio and the recently introduced tail mean variance, are special cases of this functional. The new results essentially generalize previous results by the authors concerning the maximization of combination of expected portfolio return and a function of the variance of portfolio return. Our general mean–variance functional is not restricted to a concave function with a single optimal solution. Thus, we also provide optimal solutions to a fractional programming problem, that is arising in portfolio theory. The obtained analytic solution of the optimization problem allows us to conclude that all the optimization problems corresponding to the general functional have efficient frontiers belonged to the efficient frontier obtained for the mean–variance portfolio.

KW - Concave fractional programming

KW - Elliptically distributed returns

KW - Expected utility maximization

KW - Optimal portfolio selection

KW - Sharpe ratio

KW - Two-moment decision models

UR - http://www.scopus.com/inward/record.url?scp=85084199340&partnerID=8YFLogxK

U2 - 10.1007/s10957-020-01664-3

DO - 10.1007/s10957-020-01664-3

M3 - Article

AN - SCOPUS:85084199340

VL - 185

SP - 622

EP - 651

JO - Journal of Optimization Theory and Applications

JF - Journal of Optimization Theory and Applications

SN - 0022-3239

IS - 2

ER -