---
title: Data-Driven Minimax-Regret Portfolio Optimization under Tail-Risk Ambiguity
url: https://www.ml-quant.com/papers/ssrn/7486600/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 7486600
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7486600
featured: 2026-09-25
citations: unknown
topic: Portfolio & Allocation
---


# 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.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7486600
- Identifier: SSRN 7486600
- Released: 2026-09-21
- First featured: Quant Letter No. 132 (2026-09-25): https://www.ml-quant.com/issues/2026-09-25/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation
- Authors: Zheqi Fan

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