提出新型金融时序生成评估方法，提升决策可靠性。
Nested Optimal Transport Distances
- 采用时序因果的嵌套最优传输距离衡量生成效果
- 算法计算速度显著快于现有方法，支持并行处理
- 适用于对冲、最优停止等金融决策场景
模拟真实的金融时间序列对于压力测试、情景生成和不确定性下的决策至关重要。尽管深度生成模型取得进展，但尚无统一的评估指标。本文聚焦生成式AI在金融决策中的应用，采用嵌套最优传输距离——一种具有时间因果性的最优传输变体，该度量对对冲、最优停止和强化学习等任务具有鲁棒性。此外，我们提出一种统计一致且天然可并行的计算算法，相较现有方法实现显著提速。
原文摘要 · Abstract (English)
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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