Sharing the Value-at-Risk under Distributional Ambiguity

Department of Systems Engineering and Engineering Management
The Chinese University of Hong Kong

Title: Sharing the Value-at-Risk under Distributional Ambiguity


Speaker: Professor CHEN Zhi  (City University of Hong Kong)


Venue: Room 513, William M.W. Mong Engineering Building, CUHK
Friday, January  10, 2019 - 16:30 to 17:30


We consider the problem of risk sharing, where a coalition of homogeneous agents, each bearing a random cost, aggregates their costs and shares the value-at-risk of such a risky position. Due to limited distributional information in practice, the joint distribution of agents' random costs is difficult to acquire. The coalition, being aware of the distributional ambiguity, thus evaluates the worst-case value-at-risk within a commonly agreed ambiguity set of the possible joint distributions. Through the lens of cooperative game theory, we show that this coalitional worst-case value-at-risk is subadditive for the popular ambiguity sets in the distributionally robust optimization literature that are based on convex moments or Wasserstein distance to some reference distributions. In addition, we propose easy-to-compute core allocation schemes to share the worst-case value-at-risk. Our results can be readily extended to sharing the worst-case conditional value-at-risk under distributional ambiguity.



Zhi Chen is an Assistant Professor in the Department of Management Sciences, College of Business, City University of Hong Kong. He obtained a Bachelor of Engineering degree from Tsinghua University in China, and he holds a PhD degree in Management from the National University of Singapore. He was a postdoctoral research associate in the Department of Management at the Imperial College Business School. His research interests include (1) decision-making under uncertainty with different levels of data availability and its applications in decision analysis, operations management, and engineering; (2) cooperative game theory for joint activities and its applications in production economics, resource pooling, and risk management.


Friday, January 10, 2020 - 16:30