Distributional Reinforcement Learning on Path-dependent Options

2025/07/16 by Ahmet Umur Özsoy, Özsoy, Ahmet Umur
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Mathematical Finance (q-fin.MF) #Reinforcement Learning in Robotics #Smart Grid Energy Management

paper · pdf · doi:10.48550/arxiv.2507.12657

openalex publication_date 2025/07/16 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/28

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.

Citations

• The QLBS Model within the presence of feedback loops through the impacts of a large trader
• Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning
• Implicit Quantile Networks for Distributional Reinforcement Learning
• An Analysis of Categorical Distributional Reinforcement Learning
• Distributional Reinforcement Learning with Quantile Regression
• A Distributional Perspective on Reinforcement Learning
• Learning to trade via direct reinforcement

Related

• EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning
• Exploiting Distributional Value Functions for Financial Market Valuation, Enhanced Feature Creation and Improvement of Trading Algorithms
• Option Dynamic Hedging Using Reinforcement Learning
• Equal Risk Pricing of Derivatives with Deep Hedging
• Applying Reinforcement Learning to Option Pricing and Hedging