Data-Driven Minimax-Regret Portfolio Optimization under Tail-Risk Ambiguity
The research proposes a data-driven portfolio method that blends tail-risk models and projects onto valid mixtures, providing bounds on Expected Shortfall regret without Wasserstein assumptions.
Featured in No. 132 on 25 Sep 2026 · 4 days after release
- Released
- 21 Sep 2026
- First featured
- No. 132 · 25 Sep 2026
- Published in
- Not yet, as far as Semantic Scholar knows
- Fanfare
- 2 of 5
- Identifier
- SSRN 7486600
- Authors
- Zheqi Fan
Citations and venue from Semantic Scholar (ODC-BY), refreshed weekly. Summary: Quant Letter (CC BY 4.0).