期刊
OPERATIONS RESEARCH LETTERS
卷 46, 期 4, 页码 448-452出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.orl.2018.05.005
关键词
Robust empirical optimization; Mean-variance optimization; Data-driven optimization; phi-divergence; Regularization; Bias-variance trade-off
资金
- JSPS KAKENHI [15K01204, 16H01833, 26242027]
- Natural Sciences and Engineering Research Council (NSERC) [RGPIN-2015-04019]
- Singapore Ministry of Education Academic Research Fund Tier 2 [MOE2016-T2-1-086]
- Grants-in-Aid for Scientific Research [15K01204, 16H01833, 26242027] Funding Source: KAKEN
We formulate a distributionally robust optimization problem where the deviation of the alternative distribution is controlled by a phi-divergence penalty in the objective, and show that a large class of these problems are essentially equivalent to a mean variance problem. We also show that while a small amount of robustness always reduces the in-sample expected reward, the reduction in the variance, which is a measure of sensitivity to model misspecification, is an order of magnitude larger. (C) 2018 The Authors. Published by Elsevier B.V.
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