{"ID":2873462,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06702","arxiv_id":"2509.06702","title":"Nested Optimal Transport Distances","abstract":"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.","short_abstract":"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 e...","url_abs":"https://arxiv.org/abs/2509.06702","url_pdf":"https://arxiv.org/pdf/2509.06702v1","authors":"[\"Ruben Bontorno\",\"Songyan Hou\"]","published":"2025-09-08T13:55:18Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-fin.CP\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}