Distributional Reinforcement Learning on Path-dependent Options
This addresses risk-aware pricing and uncertainty quantification for financial derivatives, representing an incremental application of existing methods to a new domain.
The paper tackles the problem of pricing path-dependent financial derivatives by estimating the full distribution of payoffs using Distributional Reinforcement Learning, demonstrating efficacy on Asian options with quantile-based approximators.
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.