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Distributionally Robust Reward-risk Ratio Programming with Wasserstein Metric

Yong Zhao (zhaoyongty***at***126.com)
Yongchao Liu (lyc***at***dlut.edu.cn)
Jin Zhang (zhangjin***at***hkbu.edu.hk)
Xinmin Yang (xmyang***at***cqnu.edu.cn)

Abstract: Reward-risk ratio (RR) is a very important stock market definition. In recent years, people extend RR model as distributionally robust reward-risk ratio (DRR) to capture the situation that the investor does not have complete information on the distribution of the underlying uncertainty. In this paper, we study the DRR model where the ambiguity on the distributions is defined through Wassertein metric. Under some moderate conditions, we show that for a fixed ratio, the DRR problem has the tractable reformulation, which motivates us to solve the problem through bisection algorithm. Specifically, we analyze the distributionally robust Sortino-Satchel ratio, Omega ratio and Stable Tail Adjusted Return ratio.

Keywords: Distributionally robust optimization, Reward-risk ratio, Wasserstein metric

Category 1: Stochastic Programming

Category 2: Robust Optimization


Download: [Postscript][PDF]

Entry Submitted: 01/09/2017
Entry Accepted: 01/10/2017
Entry Last Modified: 01/23/2017

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