Robust Optimization Approaches for Portfolio Selection: A Computational and Comparative Analysis

TL;DR

This paper conducts a comprehensive empirical comparison of robust optimization models for portfolio selection, analyzing their performance on US market data from 2005 to 2016 to address uncertainties in asset returns.

Contribution

It provides the first extensive empirical assessment of various robust optimization models in portfolio selection, comparing their effectiveness under real market data.

Findings

Robust optimization models outperform traditional methods in uncertain market conditions.

Certain risk measures within RO models yield more stable portfolios.

Performance varies significantly across different types of uncertainty sets.

Abstract

The field of portfolio selection is an active research topic, which combines elements and methodologies from various fields, such as optimization, decision analysis, risk management, data science, forecasting, etc. The modeling and treatment of deep uncertainties for future asset returns is a major issue for the success of analytical portfolio selection models. Recently, robust optimization (RO) models have attracted a lot of interest in this area. RO provides a computationally tractable framework for portfolio optimization based on relatively general assumptions on the probability distributions of the uncertain risk parameters. Thus, RO extends the framework of traditional linear and non-linear models (e.g., the well-known mean-variance model), incorporating uncertainty through a formal and analytical approach into the modeling process. Robust counterparts of existing models can be considered as worst-case re-formulations as far as deviations of the uncertain parameters from their nominal values are concerned. Although several RO models have been proposed in the literature focusing on various risk measures and different types of uncertainty sets about asset returns, analytical empirical assessments of their performance have not been performed in a comprehensive manner. The objective of this study is to fill in this gap in the literature. More specifically, we consider different types of RO models based on popular risk measures and conduct an extensive comparative analysis of their performance using data from the US market during the period 2005-2016.