mlsanityData to 14 Oct 2026

A time-stepping deep gradient flow method for option pricing in (rough) diffusion models

arXiv 2403.00746 ↗First posted 1 Mar 2024q-fin.CP cs.LG math.PRcited by 3

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Abstract (arXiv)

We develop a novel deep learning approach for pricing European options in diffusion models, that can efficiently handle high-dimensional problems resulting from Markovian approximations of rough volatility models. The option pricing partial differential equation is reformulated as an energy minimization problem, which is approximated in a time-stepping fashion by deep artificial neural networks. The proposed scheme respects the asymptotic behavior of option prices for large levels of moneyness, and adheres to a priori known bounds for option prices. The accuracy and efficiency of the proposed method is assessed in a series of numerical examples, with particular focus in the lifted Heston model.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 54 references that are in the map. First posted on arXiv 1 Mar 2024.

Builds on

5 of 54 references.

References · 5

  1. 2017DGM: A deep learning algorithm for solving partial differential equationscited by 678
  2. 2017The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problemscited by 583
  3. 2017Solving high-dimensional partial differential equations using deep learningcited by 556
  4. 2021The Deep Minimizing Movement Schemecited by 15
  5. 2024A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion modelscited by 5

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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Error Analysis of Deep PDE Solvers for Option Pricingcited by 0
  2. 2025Time Deep Gradient Flow Method for pricing American optionscited by 0
  3. 2025Convergence of the generalization error for deep gradient flow methods for PDEscited by 0

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  5. 2022Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective
  6. 2026Generative Diffusion Model for Risk-Neutral Derivative Pricing
  7. 2026Arbitrage-Aware Multi-Step Forecasting of Implied Volatility Surfaces: Modelling Surface Trajectories Using Latent Diffusion
  8. 2025Deep Reinforcement Learning Algorithms for Option Hedging