Nested Optimal Transport Distances
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866916939816763392 |
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| author | Bontorno, Ruben Hou, Songyan |
| author_facet | Bontorno, Ruben Hou, Songyan |
| contents | Simulating realistic financial time series is essential for stress testing, scenario generation, and decision-making under uncertainty. Despite advances in deep generative models, there is no consensus metric for their evaluation. We focus on generative AI for financial time series in decision-making applications and employ the nested optimal transport distance, a time-causal variant of optimal transport distance, which is robust to tasks such as hedging, optimal stopping, and reinforcement learning. Moreover, we propose a statistically consistent, naturally parallelizable algorithm for its computation, achieving substantial speedups over existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06702 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nested Optimal Transport Distances Bontorno, Ruben Hou, Songyan Machine Learning Computational Finance 91G60, 60G07, 65C60 Simulating realistic financial time series is essential for stress testing, scenario generation, and decision-making under uncertainty. Despite advances in deep generative models, there is no consensus metric for their evaluation. We focus on generative AI for financial time series in decision-making applications and employ the nested optimal transport distance, a time-causal variant of optimal transport distance, which is robust to tasks such as hedging, optimal stopping, and reinforcement learning. Moreover, we propose a statistically consistent, naturally parallelizable algorithm for its computation, achieving substantial speedups over existing approaches. |
| title | Nested Optimal Transport Distances |
| topic | Machine Learning Computational Finance 91G60, 60G07, 65C60 |
| url | https://arxiv.org/abs/2509.06702 |