Paper Info
Title
Data-Driven Optimization Of Reward-Risk Ratio Measures
Abstract
We investigate a class of fractional distributionally robust optimization problems with uncertain probabilities. They consist in the maximization of ambiguous fractional functions representing reward-risk ratios and have a semi-infinite programming epigraphic formulation. We derive a new fully parameterized closed-form to compute a new bound on the size of the Wasserstein ambiguity ball. We design a data-driven reformulation and solution framework. The reformulation phase involves the derivation of the support function of the ambiguity set and the concave conjugate of the ratio function. We design modular bisection algorithms which enjoy the finite convergence property. This class of problems has wide applicability in finance, and we specify new ambiguous portfolio optimization models for the Sharpe and Omega ratios. The computational study shows the applicability and scalability of the framework to solve quickly large, industry-relevant-size problems, which cannot be solved in one day with state-of-the-art mixed-integer nonlinear programming (MINLP) solvers.
Year
DOI
Venue
2021
10.1287/ijoc.2020.1002
INFORMS JOURNAL ON COMPUTING
Keywords
DocType
Volume
data-driven optimization, distributionally robust optimization, reward-risk ratio, Wasserstein metric, fractional programming
Journal
33
Issue
ISSN
Citations 
3
1091-9856
2
PageRank 
References 
Authors
0.37
0
2
Name
Order
Citations
PageRank
Ran Ji120.71
Miguel A. Lejeune225321.95