Title	Worst-Case Law Invariant Risk Measures and Distributions: The Case of Nonlinear DRO
Creator	Li, Jonathan Yu-Meng
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2018-03-05T14:32
Description	The class of law invariant coherent risk measures contains many risk measures that one would encounter in a distribution setting. In this talk, we present some general results about worst-case law invariant risk measures where a set of distributions sharing the same first few moments are considered for estimating the worst possible risk. In particular, its distributionally robust optimization (DRO) formulation is generally nonlinear in distribution and thus requires additional care in studying its tractability. We show cases where worst-case risk measures and distributions admit closed-form expressions and discuss their implication for future research. Our analysis exploits the structure of spectral risk measure and its connection to law invariant risk measures.
Extent	33 minutes
Subject	Mathematics; Operations research, mathematical programming; Probability theory and stochastic processes; Operation research
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: University of Ottawa
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2018-09-02
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0371881
URI	http://hdl.handle.net/2429/67065
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Researcher
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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Worst-Case Law Invariant Risk Measures and Distributions: The Case of Nonlinear DRO Li, Jonathan Yu-Meng

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