Deep-learning based numerical BSDE method for barrier options
Abstract
As is known, an option price is a solution to a certain partial differential equation (PDE) with terminal conditions (payoff functions). There is a close association between the solution of PDE and the solution of a backward stochastic differential equation (BSDE). We can either solve the PDE to obtain option prices or solve its associated BSDE. Recently a deep learning technique has been applied to solve option prices using the BSDE approach. In this approach, deep learning is used to learn some deterministic functions, which are used in solving the BSDE with terminal conditions. In this paper, we extend the deep-learning technique to solve a PDE with both terminal and boundary conditions. In particular, we will employ the technique to solve barrier options using Brownian motion bridges.
Cite
@article{arxiv.1904.05921,
title = {Deep-learning based numerical BSDE method for barrier options},
author = {Bing Yu and Xiaojing Xing and Agus Sudjianto},
journal= {arXiv preprint arXiv:1904.05921},
year = {2019}
}