A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Kapllani, Lorenc, Teng, Long |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
A forward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
par: Kapllani, Lorenc, et autres
Publié: (2024)
par: Kapllani, Lorenc, et autres
Publié: (2024)
Deep learning algorithms for solving high dimensional nonlinear backward stochastic differential equations
par: Kapllani, Lorenc, et autres
Publié: (2020)
par: Kapllani, Lorenc, et autres
Publié: (2020)
Multistep schemes for solving backward stochastic differential equations on GPU
par: Kapllani, Lorenc, et autres
Publié: (2019)
par: Kapllani, Lorenc, et autres
Publié: (2019)
A deep solver for backward stochastic Volterra integral equations
par: Andersson, Kristoffer, et autres
Publié: (2025)
par: Andersson, Kristoffer, et autres
Publié: (2025)
Error analysis for learning fractional stochastic differential equations with applications in neural approximations
par: Dehshiri, Mahdi, et autres
Publié: (2026)
par: Dehshiri, Mahdi, et autres
Publié: (2026)
A deep BSDE approach for the simultaneous pricing and delta-gamma hedging of large portfolios consisting of high-dimensional multi-asset Bermudan options
par: Negyesi, Balint, et autres
Publié: (2025)
par: Negyesi, Balint, et autres
Publié: (2025)
A new architecture of high-order deep neural networks that learn martingales
par: Ninomiya, Syoiti, et autres
Publié: (2025)
par: Ninomiya, Syoiti, et autres
Publié: (2025)
Error analysis for empirical risk minimization over clipped ReLU networks in solving linear Kolmogorov partial differential equations
par: Xiao, Jichang, et autres
Publié: (2023)
par: Xiao, Jichang, et autres
Publié: (2023)
The Compound BSDE Method: A Fully Forward Method for Option Pricing and Optimal Stopping Problems in Finance
par: Huang, Zhipeng, et autres
Publié: (2026)
par: Huang, Zhipeng, et autres
Publié: (2026)
A forward scheme with machine learning for forward-backward SDEs with jumps by decoupling jumps
par: Kawai, Reiichiro, et autres
Publié: (2024)
par: Kawai, Reiichiro, et autres
Publié: (2024)
Data-driven computation for periodic stochastic differential equations
par: Li, Yao, et autres
Publié: (2025)
par: Li, Yao, et autres
Publié: (2025)
Functional SDE approximation inspired by a deep operator network architecture
par: Eigel, Martin, et autres
Publié: (2024)
par: Eigel, Martin, et autres
Publié: (2024)
A high-order recombination algorithm for weak approximation of stochastic differential equations
par: Ninomiya, Syoiti, et autres
Publié: (2025)
par: Ninomiya, Syoiti, et autres
Publié: (2025)
A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models
par: Georgoulis, Emmanuil H., et autres
Publié: (2024)
par: Georgoulis, Emmanuil H., et autres
Publié: (2024)
Discontinuous hybrid neural networks for the one-dimensional partial differential equations
par: Wang, Xiaoyu, et autres
Publié: (2025)
par: Wang, Xiaoyu, et autres
Publié: (2025)
A deep backward regression-based scheme for high-dimensional nonlinear partial differential equations
par: Han, Qiang, et autres
Publié: (2026)
par: Han, Qiang, et autres
Publié: (2026)
Deep learning interpretability for rough volatility
par: Yuan, Bo, et autres
Publié: (2024)
par: Yuan, Bo, et autres
Publié: (2024)
Hedging of exotic options in Hawkes jump-diffusion models by Malliavin calculus
par: Ahmadi, Ayub, et autres
Publié: (2025)
par: Ahmadi, Ayub, et autres
Publié: (2025)
Data-Free Asymptotics-Informed Operator Networks for Singularly Perturbed PDEs
par: Lee, Jinsil, et autres
Publié: (2025)
par: Lee, Jinsil, et autres
Publié: (2025)
Uniform in time convergence of numerical schemes for stochastic differential equations via Strong Exponential stability: Euler methods, Split-Step and Tamed Schemes
par: Angeli, Letizia, et autres
Publié: (2023)
par: Angeli, Letizia, et autres
Publié: (2023)
On the short-time behaviour of up-and-in barrier options using Malliavin calculus
par: Burés, Òscar
Publié: (2025)
par: Burés, Òscar
Publié: (2025)
Strong solution of stochastic differential equations with discontinuous and unbounded coefficients
par: Hu, Yaozhong, et autres
Publié: (2023)
par: Hu, Yaozhong, et autres
Publié: (2023)
Uncertainty-Aware Deep Hedging
par: Poddar, Manan
Publié: (2026)
par: Poddar, Manan
Publié: (2026)
Score-based constrained generative modeling via Langevin diffusions with boundary conditions
par: Nordenhög, Adam, et autres
Publié: (2025)
par: Nordenhög, Adam, et autres
Publié: (2025)
Deep Quadratic Hedging
par: Gnoatto, Alessandro, et autres
Publié: (2022)
par: Gnoatto, Alessandro, et autres
Publié: (2022)
Machine learning for option pricing: an empirical investigation of network architectures
par: Della Corte, Serena, et autres
Publié: (2023)
par: Della Corte, Serena, et autres
Publié: (2023)
Prediction of discretization of online GMsFEM using deep learning for Richards equation
par: Spiridonov, Denis, et autres
Publié: (2024)
par: Spiridonov, Denis, et autres
Publié: (2024)
Universal approximation property of neural stochastic differential equations
par: Kwossek, Anna P., et autres
Publié: (2025)
par: Kwossek, Anna P., et autres
Publié: (2025)
Convergence proofs and strong error bounds for forward-backward stochastic differential equations using neural network simulations
par: Sheridan-Methven, Oliver
Publié: (2024)
par: Sheridan-Methven, Oliver
Publié: (2024)
A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
par: Papapantoleon, Antonis, et autres
Publié: (2024)
par: Papapantoleon, Antonis, et autres
Publié: (2024)
Coupling of forward-backward stochastic differential equations on the Wiener space, and application on regularity
par: Zhou, Xilin
Publié: (2025)
par: Zhou, Xilin
Publié: (2025)
A deep solver for BSDEs with jumps
par: Andersson, Kristoffer, et autres
Publié: (2022)
par: Andersson, Kristoffer, et autres
Publié: (2022)
Discontinuous Galerkin finite element operator network for solving non-smooth PDEs
par: Chawla, Kapil, et autres
Publié: (2026)
par: Chawla, Kapil, et autres
Publié: (2026)
The deep multi-FBSDE method: a robust deep learning method for coupled FBSDEs
par: Andersson, Kristoffer, et autres
Publié: (2025)
par: Andersson, Kristoffer, et autres
Publié: (2025)
Error Analysis of Deep PDE Solvers for Option Pricing
par: Rou, Jasper
Publié: (2025)
par: Rou, Jasper
Publié: (2025)
Short-time behavior of the At-The-Money implied volatility for the jump-diffusion stochastic volatility Bachelier model
par: Alòs, Elisa, et autres
Publié: (2025)
par: Alòs, Elisa, et autres
Publié: (2025)
High-dimensional Bayesian filtering through deep density approximation
par: Bågmark, Kasper, et autres
Publié: (2025)
par: Bågmark, Kasper, et autres
Publié: (2025)
An energy-based deep splitting method for the nonlinear filtering problem
par: Bågmark, Kasper, et autres
Publié: (2022)
par: Bågmark, Kasper, et autres
Publié: (2022)
In-Context Operator Learning on the Space of Probability Measures
par: Cole, Frank, et autres
Publié: (2026)
par: Cole, Frank, et autres
Publié: (2026)
A discontinuous Galerkin plane wave neural network method for Helmholtz equation and Maxwell's equations
par: Yuan, Long, et autres
Publié: (2025)
par: Yuan, Long, et autres
Publié: (2025)
Documents similaires
-
A forward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
par: Kapllani, Lorenc, et autres
Publié: (2024) -
Deep learning algorithms for solving high dimensional nonlinear backward stochastic differential equations
par: Kapllani, Lorenc, et autres
Publié: (2020) -
Multistep schemes for solving backward stochastic differential equations on GPU
par: Kapllani, Lorenc, et autres
Publié: (2019) -
A deep solver for backward stochastic Volterra integral equations
par: Andersson, Kristoffer, et autres
Publié: (2025) -
Error analysis for learning fractional stochastic differential equations with applications in neural approximations
par: Dehshiri, Mahdi, et autres
Publié: (2026)