Abstract
We study Kusuoka representations of law-invariant coherent risk measures on the space of bounded random variables, which says that any law-invariant coherent risk measure is the supremum of integrals of Average-Value-at-Risk measures. We refine this representation by showing that the supremum in Kusuoka representation is attained for some probability measure in the unit interval. Namely, we prove that any law-invariant coherent risk measure on the space of bounded random variables can be written as an integral of the Average-Value-at-Risk measures on the unit interval with respect to some probability measure. This representation gives a numerically constructive way to bound any law-invariant coherent risk measure on the space of essentially bounded random variables from above and below. The results are illustrated on specific law-invariant coherent risk measures along with numerical simulations.
| Original language | English |
|---|---|
| Pages (from-to) | 3351-3362 |
| Number of pages | 12 |
| Journal | Optimization |
| Volume | 71 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 2022 |
| Externally published | Yes |
Funding
The author has been graciously funded by the Social Policy Grant and Competitive Research Grant of Nazarbayev University. The author is grateful for many insightful discussions with Alexander Shapiro during the preparation of this manuscript.
Keywords
- average value-at-risk
- Coherent risk measures
- comonotonic risk measures
- law invariance
- robust performance measures
ASJC Scopus subject areas
- Control and Optimization
- Management Science and Operations Research
- Applied Mathematics
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