Computationally tractable counterparts of distributionally robust constraints on risk measures

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In optimization problems appearing in fields such as economics, finance, or engineering, it is often important that a risk measure of a decision-dependent random variable stays below a prescribed level. At the same time, the underlying probability distribution determining the risk measure's value is typically known only up to a certain degree and the constraint should hold for a reasonably wide class of probability distributions. In addition, the constraint should be computationally tractable. In this paper we review and generalize results on the derivation of tractable counterparts of such constraints for discrete probability distributions. Using established techniques in robust optimization, we show that the derivation of a tractable robust counterpart can be split into two parts, one corresponding to the risk measure and the other to the uncertainty set. This holds for a wide range of risk measures and uncertainty sets for probability distributions defined using statistical goodness-of-fit tests or probability metrics. In this way, we provide a unified framework for reformulating this class of constraints, extending the number of solvable risk measure-uncertainty set combinations considerably, also including risk measures that are nonlinear in the probabilities. To provide a clear overview for the user, we provide the computational tractability status for each of the uncertainty set-risk measure pairs, some of which have been solved in the literature. Examples, including portfolio optimization and antenna array design, illustrate the proposed approach in a theoretical and numerical setting.



Original languageEnglish
Pages (from-to)603-650
Number of pages48
JournalSIAM Review
Volume58
Issue number4
DOIs
Publication statusPublished - 3 Nov 2016

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Risk Measures
Probability distributions
Probability Distribution
Uncertainty
Probability Metrics
Portfolio Optimization
Dependent Random Variables
Antenna Arrays
Robust Optimization
Tractability
Discrete Distributions
Goodness of Fit Test
Finance
Statistical test
Antenna arrays
Random variables
Economics
Optimization Problem
Engineering
Generalise

Keywords

  • risk measure
  • robust counterpart
  • nonlinear inequality
  • robust optimization
  • support functions

Cite this

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title = "Computationally tractable counterparts of distributionally robust constraints on risk measures",
abstract = "In optimization problems appearing in fields such as economics, finance, or engineering, it is often important that a risk measure of a decision-dependent random variable stays below a prescribed level. At the same time, the underlying probability distribution determining the risk measure's value is typically known only up to a certain degree and the constraint should hold for a reasonably wide class of probability distributions. In addition, the constraint should be computationally tractable. In this paper we review and generalize results on the derivation of tractable counterparts of such constraints for discrete probability distributions. Using established techniques in robust optimization, we show that the derivation of a tractable robust counterpart can be split into two parts, one corresponding to the risk measure and the other to the uncertainty set. This holds for a wide range of risk measures and uncertainty sets for probability distributions defined using statistical goodness-of-fit tests or probability metrics. In this way, we provide a unified framework for reformulating this class of constraints, extending the number of solvable risk measure-uncertainty set combinations considerably, also including risk measures that are nonlinear in the probabilities. To provide a clear overview for the user, we provide the computational tractability status for each of the uncertainty set-risk measure pairs, some of which have been solved in the literature. Examples, including portfolio optimization and antenna array design, illustrate the proposed approach in a theoretical and numerical setting.",
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Computationally tractable counterparts of distributionally robust constraints on risk measures. / Postek, Krzysztof; den Hertog, Dick; Melenberg, Bertrand.

In: SIAM Review, Vol. 58, No. 4, 03.11.2016, p. 603-650.

Research output: Contribution to journalArticleScientificpeer-review

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