- Worst-Case Expected Shortfall with Univariate and Bivariate Marginals Anulekha Dhara(anulekhasutd.edu.sg) Bikramjit Das(bikramsutd.edu.sg) Karthik Natarajan(karthik_natarajansutd.edu.sg) Abstract: Worst-case bounds on the expected shortfall risk given only limited information on the distribution of the random variables has been studied extensively in the literature. In this paper, we develop a new worst-case bound on the expected shortfall when the univariate marginals are known exactly and additional expert information is available in terms of bivariate marginals. Such expert information allows for one to choose from among the many possible parametric families of bivariate copulas. By considering a neighborhood of distance $\rho$ around the bivariate marginals with the Kullback-Leibler divergence measure, we model the trade-off between conservatism in the worst-case risk measure and confidence in the expert information. Our bound is developed when the only information available on the bivariate marginals forms a tree structure in which case it is efficiently computable using convex optimization. For consistent marginals, as $\rho$ approaches $\infty$, the bound reduces to the comonotonic upper bound and as $\rho$ approaches $0$, the bound reduces to the worst-case bound with bivariates known exactly. We also discuss extensions to inconsistent marginals and instances where the expert information which might be captured using other parameters such as correlations. Keywords: expected shortfall (CVaR), distributionally robust bound, marginals, KL-divergence Category 1: Applications -- OR and Management Sciences (Finance and Economics ) Citation: Engineering Systems and Design, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372. January, 2017. Download: [PDF]Entry Submitted: 01/15/2017Entry Accepted: 01/17/2017Entry Last Modified: 01/15/2017Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.