A Composite Risk Measure Framework for Decision Making Under Uncertainty

Peng Yu Qian, Zi Zhuo Wang, Zai Wen Wen

Research output: Contribution to journalArticle

Abstract

In this paper, we present a unified framework for decision making under uncertainty. Our framework is based on the composite of two risk measures, where the inner risk measure accounts for the risk of decision if the exact distribution of uncertain model parameters were given, and the outer risk measure quantifies the risk that occurs when estimating the parameters of distribution. We show that the model is tractable under mild conditions. The framework is a generalization of several existing models, including stochastic programming, robust optimization, distributionally robust optimization. Using this framework, we study a few new models which imply probabilistic guarantees for solutions and yield less conservative results compared to traditional models. Numerical experiments are performed on portfolio selection problems to demonstrate the strength of our models.

Original languageEnglish (US)
Pages (from-to)43-68
Number of pages26
JournalJournal of the Operations Research Society of China
Volume7
Issue number1
DOIs
StatePublished - Mar 6 2019

Fingerprint

Risk measures
Decision making under uncertainty
Robust optimization
Portfolio selection
Guarantee
Numerical experiment
Stochastic programming

Keywords

  • Portfolio management
  • Risk management
  • Stochastic programming

Cite this

A Composite Risk Measure Framework for Decision Making Under Uncertainty. / Qian, Peng Yu; Wang, Zi Zhuo; Wen, Zai Wen.

In: Journal of the Operations Research Society of China, Vol. 7, No. 1, 06.03.2019, p. 43-68.

Research output: Contribution to journalArticle

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