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Risk management in multi-objective portfolio optimization under uncertainty

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  • Yannick Becker
  • Pascal Halffmann
  • Anita Schobel

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.

Suggested Citation

  • Yannick Becker & Pascal Halffmann & Anita Schobel, 2024. "Risk management in multi-objective portfolio optimization under uncertainty," Papers 2407.19936, arXiv.org.
    • Handle: RePEc:arx:papers:2407.19936
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    File URL: http://arxiv.org/pdf/2407.19936
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    References listed on IDEAS

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    1. Schöbel, Anita & Zhou-Kangas, Yue, 2021. "The price of multiobjective robustness: Analyzing solution sets to uncertain multiobjective problems," European Journal of Operational Research, Elsevier, vol. 291(2), pages 782-793.
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