Risk management in multi-objective portfolio optimization under uncertainty
Abstract
In portfolio optimization, decision makers face difficulties from uncertainties inherent in real-world scenarios. These uncertainties significantly influence portfolio outcomes in both classical and multi-objective Markowitz models. To address these challenges, our research explores the power of robust multi-objective optimization. Since portfolio managers frequently measure their solutions against benchmarks, we enhance the multi-objective min-regret robustness concept by incorporating these benchmark comparisons. This approach bridges the gap between theoretical models and real-world investment scenarios, offering portfolio managers more reliable and adaptable strategies for navigating market uncertainties. Our framework provides a more nuanced and practical approach to portfolio optimization under real-world conditions.
Keywords
Cite
@article{arxiv.2407.19936,
title = {Risk management in multi-objective portfolio optimization under uncertainty},
author = {Yannick Becker and Pascal Halffmann and Anita Schöbel},
journal= {arXiv preprint arXiv:2407.19936},
year = {2026}
}