An Efficient Scenario Reduction Method for Problems with Higher Moment Coherent Risk Measures

Xiaolei He () and Weiguo Zhang ()
Additional contact information
Xiaolei He: College of Management, Shenzhen University, Shenzhen 518055, China
Weiguo Zhang: College of Management, Shenzhen University, Shenzhen 518055, China

INFORMS Journal on Computing, 2025, vol. 37, issue 3, 743-760

Abstract: In this paper, we present an efficient scenario reduction method for optimization problems whose objective function is the higher moment coherent risk (HMCR) measures. Compared with existing approaches, our method places greater emphasis on the characteristics of optimization problems. Because the value of HMCR measures only depends on a small subset of scenarios that correspond to high cost or loss, our approach is based mostly on the concept of ineffective scenarios previously proposed in the literature, which entails identifying the scenarios whose removal from the problem results in no change of the optimal value. We test our method on a simple portfolio optimization problem with only nine risky assets and a realistic one with 50 risky assets and cardinality constraints. Results show that our scenario reduction method can yield a more accurate optimal solution and optimal value, along with a smaller reduced-scenario set. Even at the same reduction level, our method continues to outperform the existing scenario reduction methods. Interestingly, the portfolio produced by our method is also more diversified than others.

Keywords: scenario reduction method; higher moment coherent risk measures; ineffective scenarios; portfolio optimization problem (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/ijoc.2022.0375 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:37:y:2025:i:3:p:743-760

Access Statistics for this article

More articles in INFORMS Journal on Computing from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().