Portfolio Optimization with Higher-Order Stochastic Dominance Constraints

41 Pages Posted: 14 May 2019 Last revised: 16 Jul 2019

See all articles by Yi Fang

Yi Fang

Jilin University (JLU) - Center for Quantitative Economics

Thierry Post

Graduate School of Business of Nazarbayev University

Date Written: April 23, 2019

Abstract

A framework is developed for portfolio optimization with higher-order Stochastic Dominance constraints. A finite system of restrictions on the lower partial moments can be used for evaluating the efficiency of a given benchmark and for constructing enhanced portfolios which dominate the benchmark for the relevant class of risk averters. The system can be linearlized for discrete distributions, allowing for implementation using Linear Programming. Imposing higher-order restrictions expands the set of improvement possibilities and mitigates the risk of suboptimality due to estimation error. A simulation study exemplifies these benefits, for a simple hypothetical investment problem. In an empirical application to equity industry rotation, accounting for kurtosis aversion and Decreasing Absolute Prudence improves out-of-sample performance beyond levels achieved using more permissive assumptions.

Keywords: Portfolio Choice, Higher-Order Risk, Stochastic Dominance, Linear Programming, Enhanced Indexing, Industry Rotation

JEL Classification: C61, D81, G11

Suggested Citation

Fang, Yi and Post, Thierry, Portfolio Optimization with Higher-Order Stochastic Dominance Constraints (April 23, 2019). Available at SSRN: https://ssrn.com/abstract=3376468 or http://dx.doi.org/10.2139/ssrn.3376468

Yi Fang

Jilin University (JLU) - Center for Quantitative Economics ( email )

Changchun, Jilin 130012
China

Thierry Post (Contact Author)

Graduate School of Business of Nazarbayev University ( email )

53 Kabanbay Batyra Avenue
Astana, 010000
Kazakhstan

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