Distributionally robust expectation inequalities for structured distributions

B. Van Parys, P. J. Goulart and M. Morari

Mathematical Programming, vol. 2019, no. 173, pp. 251-280, January 2019.
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  author = {B. Van Parys and P. J. Goulart and M. Morari},
  title = {Distributionally robust expectation inequalities for structured distributions},
  journal = {Mathematical Programming},
  year = {2019},
  volume = {2019},
  number = {173},
  pages = {251-280},
  url = {https://doi.org/10.1007/s10107-017-1220-x},
  doi = {10.1007/s10107-017-1220-x}

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.