Paper: arXiv 2507.12657

Authors: Ahmet Umur Özsoy

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper provides a strong, mathematically sound theoretical framework connecting Distributional Reinforcement Learning and RBF quantile approximation to path-dependent option pricing. However, as a preliminary draft, it lacks extensive empirical backtesting, code availability, and complete experimental results.

Research Flowchart

  flowchart TD
    A[Research Goal: Risk-aware pricing & tail-risk estimation for path-dependent options] --> B[Inputs: Underlying Asset Paths & Asian Option Characteristics]
    B --> C[Methodology: Distributional Reinforcement Learning Framework]
    C --> D[Computation: Quantile-based Value Function Approximators]
    D --> E[Process: Estimate Full Conditional Payoff Distribution]
    E --> F[Outcomes: Precise Option Pricing, Uncertainty Quantification & Tail-Risk Metrics]

Cited References (17)

  1. Distributional Reinforcement Learning with Quantile Regression (2017) — link
  2. A Distributional Perspective on Reinforcement Learning (2017) — link
  3. A PDE approach to Asian options: analytical and numerical evidence (1997)
  4. The value of an Asian option (1995)
  5. A new PDE approach for pricing arithmetic average Asian options (2001)
  6. An analysis of categorical distributional reinforcement learning (2018)
  7. Deep reinforcement learning for option pricing and hedging under dynamic expectile risk measures (2023)
  8. Deep reinforcement learning for option replication and hedging (2020)
and 9 more
  1. Deep Hedging of Derivatives Using Reinforcement Learning (2020) — link
  2. The QLBS Model within the presence of feedback loops through the impacts of a large trader — → 2311.06790
  3. Gamma and vega hedging using deep distributional reinforcement learning (2023)
  4. Stochastic calculus for finance II: Continuous-time models (2004)
  5. Implicit quantile networks for distributional reinforcement learning (2018)
  6. Stochastic approximation and recursive algorithm and applications (1997)
  7. Reinforcement learning: An introduction second edition (2018)
  8. Learning to trade via direct reinforcement (2001)
  9. Reinforcement learning for optimized trade execution (2006)

1 of 17 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.