Cover Image

A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Georgoulis, Emmanuil H., Papapantoleon, Antonis, Smaragdakis, Costas
Format:	Preprint
Published:	2024
Subjects:	Computational Finance Machine Learning Numerical Analysis Probability 68T07, 65C20, 91G60, 91G20, 65M12
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
by: Papapantoleon, Antonis, et al.
Published: (2024)

Machine learning for option pricing: an empirical investigation of network architectures
by: Della Corte, Serena, et al.
Published: (2023)

Quasi-Monte Carlo with Domain Transformation for Efficient Fourier Pricing of Multi-Asset Options
by: Bayer, Christian, et al.
Published: (2024)

A deep BSDE approach for the simultaneous pricing and delta-gamma hedging of large portfolios consisting of high-dimensional multi-asset Bermudan options
by: Negyesi, Balint, et al.
Published: (2025)

Hedging of exotic options in Hawkes jump-diffusion models by Malliavin calculus
by: Ahmadi, Ayub, et al.
Published: (2025)

Time Deep Gradient Flow Method for pricing American options
by: Rou, Jasper
Published: (2025)

The Compound BSDE Method: A Fully Forward Method for Option Pricing and Optimal Stopping Problems in Finance
by: Huang, Zhipeng, et al.
Published: (2026)

Improved model-free bounds for multi-asset options using option-implied information and deep learning
by: Dragazi, Evangelia, et al.
Published: (2024)

A forward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)

A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)

Enhancing Fourier pricing with machine learning
by: Junike, Gero, et al.
Published: (2024)

Gradient-enhanced sparse Hermite polynomial expansions for pricing and hedging high-dimensional American options
by: Yang, Jiefei, et al.
Published: (2024)

Deep learning interpretability for rough volatility
by: Yuan, Bo, et al.
Published: (2024)

Uncertainty-Aware Deep Hedging
by: Poddar, Manan
Published: (2026)

Generalized measure Black-Scholes equation: Towards option self-similar pricing
by: Riane, Nizar, et al.
Published: (2024)

Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities
by: Bayer, Christian, et al.
Published: (2020)

Correct implied volatility shapes and reliable pricing in the rough Heston model
by: Boyarchenko, Svetlana, et al.
Published: (2024)

Fast reliable pricing and calibration of the rough Heston model
by: Boyarchenko, Svetlana, et al.
Published: (2025)

Rough volatility, path-dependent PDEs and weak rates of convergence
by: Bonesini, Ofelia, et al.
Published: (2023)

Deep g-Pricing for CSI 300 Index Options with Volatility Trajectories and Market Sentiment
by: Zhang, Yilun, et al.
Published: (2026)

Low-rank kernel methods for American option pricing
by: Multerer, Michael, et al.
Published: (2026)

Fast and explicit European option pricing under tempered stable processes
by: Agazzotti, Gaetano, et al.
Published: (2025)

Numerical analysis of American option pricing in a two-asset jump-diffusion model
by: Zhou, Hao, et al.
Published: (2024)

Extreme ATM skew in a local volatility model with discontinuity: joint density approach
by: Gairat, Alexander, et al.
Published: (2023)

Efficient simulation of a new class of Volterra-type SDEs
by: Bonesini, Ofelia, et al.
Published: (2023)

Efficient and accurate simulation of the stochastic-alpha-beta-rho model
by: Choi, Jaehyuk, et al.
Published: (2024)

Pricing and calibration in the 4-factor path-dependent volatility model
by: Gazzani, Guido, et al.
Published: (2024)

Optimal strategy and deep hedging for share repurchase programs
by: Corti, Stefano, et al.
Published: (2026)

Short-time behavior of the At-The-Money implied volatility for the jump-diffusion stochastic volatility Bachelier model
by: Alòs, Elisa, et al.
Published: (2025)

Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing
by: Becker, Sebastian, et al.
Published: (2022)

Error Analysis of Deep PDE Solvers for Option Pricing
by: Rou, Jasper
Published: (2025)

A pure dual approach for hedging Bermudan options
by: Alfonsi, Aurélien, et al.
Published: (2024)

Machine-learning regression methods for American-style path-dependent contracts
by: Gambara, Matteo, et al.
Published: (2023)

In-Context Operator Learning on the Space of Probability Measures
by: Cole, Frank, et al.
Published: (2026)

Random neural networks for rough volatility
by: Jacquier, Antoine, et al.
Published: (2023)

Optimal risk mitigation by deep reinsurance
by: Arandjelović, Aleksandar, et al.
Published: (2024)

Analytic approximation for Bachelier option prices and applications
by: Alòs, Elisa, et al.
Published: (2026)

Runge--Kutta numerical methods for ruin probabilities in classical risk model
by: Kanakoudis, George, et al.
Published: (2026)

Rough differential equations for volatility
by: Bonesini, Ofelia, et al.
Published: (2024)

Almost-Exact Simulation Scheme for Heston-type Models: Bermudan and American Option Pricing
by: Dimitrov, Mara Kalicanin, et al.
Published: (2025)