Robust empirical optimization is almost the same as mean–variance optimization
| Autor: | Jun-ya Gotoh, Andrew Lim, Michael Jong Kim |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
050208 finance 021103 operations research Applied Mathematics 05 social sciences 0211 other engineering and technologies 02 engineering and technology Variance (accounting) Management Science and Operations Research Measure (mathematics) Industrial and Manufacturing Engineering Reduction (complexity) Distribution (mathematics) Robustness (computer science) 0502 economics and business Sensitivity (control systems) Divergence (statistics) Software Order of magnitude Mathematics |
| Zdroj: | Operations Research Letters. 46:448-452 |
| ISSN: | 0167-6377 |
| Popis: | We formulate a distributionally robust optimization problem where the deviation of the alternative distribution is controlled by a ϕ -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. |
| Databáze: | OpenAIRE |
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