Nested Optimal Transport Distances

• URL: http://arxiv.org/abs/2509.06702v1
• Date: Mon, 08 Sep 2025 13:55:18 GMT
• Title: Nested Optimal Transport Distances
• Authors: Ruben Bontorno, Songyan Hou,
• Abstract summary: We focus on generative AI for financial time series in decision-making applications.<n>We 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.
• Score: 0.48127184936824546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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