Pricing Barrier Options with DeepBSDEs

TL;DR

This paper introduces a new deep learning-based method to price barrier options by directly solving the associated forward-backward stochastic differential equations, effectively handling boundary conditions in high-dimensional settings.

Contribution

It extends forward deep BSDE methods to explicitly incorporate barrier boundary conditions, enabling accurate pricing of barrier options in complex, high-dimensional models.

Findings

Handles any barrier condition in FBSDE framework

Applicable to high-dimensional problems

Accurately captures boundary conditions in pricing

Abstract

This paper presents a novel and direct approach to price boundary and final-value problems, corresponding to barrier options, using forward deep learning to solve forward-backward stochastic differential equations (FBSDEs). Barrier instruments are instruments that expire or transform into another instrument if a barrier condition is satisfied before maturity; otherwise they perform like the instrument without the barrier condition. In the PDE formulation, this corresponds to adding boundary conditions to the final value problem. The deep BSDE methods developed so far have not addressed barrier/boundary conditions directly. We extend the forward deep BSDE to the barrier condition case by adding nodes to the computational graph to explicitly monitor the barrier conditions for each realization of the dynamics as well as nodes that preserve the time, state variables, and trading strategy value at barrier breach or at maturity otherwise. Given these additional nodes in the computational graph, the forward loss function quantifies the replication of the barrier or final payoff according to a chosen risk measure such as squared sum of differences. The proposed method can handle any barrier condition in the FBSDE set-up and any Dirichlet boundary conditions in the PDE set-up, both in low and high dimensions.