用分布强化学习精准定价路径依赖期权，更好捕捉风险与不确定性
Distributional Reinforcement Learning on Path-dependent Options
- 用分布强化学习建模期权收益的完整分布
- 在亚式期权上实现更优的风险估计和不确定性量化
- 适合关注尾部风险与稳健定价的研究者
我们重新诠释并提出一种基于分布强化学习（DistRL）的路径依赖金融衍生品定价框架，通过估计收益的完整分布来替代传统方法仅关注期望值的做法。该方法能实现风险感知定价、尾部风险估算及更精确的不确定性量化。我们在亚式期权上验证了该方法的有效性，采用分位数价值函数近似器进行建模。
原文摘要 · Abstract (English)
We reinterpret and propose a framework for pricing path-dependent financial derivatives by estimating the full distribution of payoffs using Distributional Reinforcement Learning (DistRL). Unlike traditional methods that focus on expected option value, our approach models the entire conditional distribution of payoffs, allowing for risk-aware pricing, tail-risk estimation, and enhanced uncertainty quantification. We demonstrate the efficacy of this method on Asian options, using quantile-based value function approximators.
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