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Distributional Reinforcement Learning on Path-dependent Options

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  • Ahmet Umur Ozsoy

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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  • Ahmet Umur Ozsoy, 2025. "Distributional Reinforcement Learning on Path-dependent Options," Papers 2507.12657, arXiv.org.
    • Handle: RePEc:arx:papers:2507.12657
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    File URL: http://arxiv.org/pdf/2507.12657
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    References listed on IDEAS

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    1. Jay Cao & Jacky Chen & Soroush Farghadani & John Hull & Zissis Poulos & Zeyu Wang & Jun Yuan, 2022. "Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning," Papers 2205.05614, arXiv.org, revised Jan 2023.
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