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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2307.03927 |
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| _version_ | 1866910684014444544 |
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| author | Multerer, Michael Schneider, Paul Sen, Rohan |
| author_facet | Multerer, Michael Schneider, Paul Sen, Rohan |
| contents | We seek to extract a small number of representative scenarios from large panel data that are consistent with sample moments. Among two novel algorithms, the first identifies scenarios that have not been observed before, and comes with a scenario-based representation of covariance matrices. The second proposal selects important data points from states of the world that have already realized, and are consistent with higher-order sample moment information. Both algorithms are efficient to compute and lend themselves to consistent scenario-based modeling and multi-dimensional numerical integration that can be used for interpretable decision-making under uncertainty. Extensive numerical benchmarking studies and an application in portfolio optimization favor the proposed algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_03927 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fast Empirical Scenarios Multerer, Michael Schneider, Paul Sen, Rohan Machine Learning Numerical Analysis Risk Management 11C20, 41A55, 46E22, 46N30, 60-08, 68W25 We seek to extract a small number of representative scenarios from large panel data that are consistent with sample moments. Among two novel algorithms, the first identifies scenarios that have not been observed before, and comes with a scenario-based representation of covariance matrices. The second proposal selects important data points from states of the world that have already realized, and are consistent with higher-order sample moment information. Both algorithms are efficient to compute and lend themselves to consistent scenario-based modeling and multi-dimensional numerical integration that can be used for interpretable decision-making under uncertainty. Extensive numerical benchmarking studies and an application in portfolio optimization favor the proposed algorithms. |
| title | Fast Empirical Scenarios |
| topic | Machine Learning Numerical Analysis Risk Management 11C20, 41A55, 46E22, 46N30, 60-08, 68W25 |
| url | https://arxiv.org/abs/2307.03927 |