Distributional Reinforcement Learning on Path-dependent Options

Ahmet Umur Özsoy

2025-07-16Uncertainty Quantification Reinforcement Learning reinforcement-learning

Abstract

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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