Distributionally robust expectation inequalities for structured distributions
Author(s)
Morari, Manfred; Goulart, Paul J.; Van Parys, Bart Paul Gerard
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Quantifying the risk of unfortunate events occurring, despite limited distributional information, is a basic problem underlying many practical questions. Indeed, quantifying constraint violation probabilities in distributionally robust programming or judging the risk of financial positions can both be seen to involve risk quantification under distributional ambiguity. In this work we discuss worst-case probability and conditional value-at-risk problems, where the distributional information is limited to second-order moment information in conjunction with structural information such as unimodality and monotonicity of the distributions involved. We indicate how exact and tractable convex reformulations can be obtained using standard tools from Choquet and duality theory. We make our theoretical results concrete with a stock portfolio pricing problem and an insurance risk aggregation example. Keywords: Optimal inequalities, Extreme distributions, Convex optimisation, Choquet representation, CVaR
Date issued
2017-12Department
Massachusetts Institute of Technology. Operations Research Center; Sloan School of ManagementJournal
Mathematical Programming
Publisher
Springer Berlin Heidelberg
Citation
Van Parys, Bart P. G., Paul J. Goulart, and Manfred Morari. “Distributionally Robust Expectation Inequalities for Structured Distributions.” Mathematical Programming 173, no. 1–2 (December 23, 2017): 251–280.
Version: Author's final manuscript
ISSN
0025-5610
1436-4646