Neural Network Methods in the 2BSDE Formalism for Solving the High-Dimensional Black--Scholes Equation with Transaction Costs
Guhesh Kumaran
Abstract
A 2BSDE system is written down for a $(D+1)$-dimensional Black--Scholes partial differential equation (PDE) with the Leland transaction cost model which incorporates the sign of the curvature. The 2BSDE system takes input from the ``speed'' (derivative of the curvature). This system is solved using a single ResNet neural network which learns both the solution to the PDE as well as the degrees of freedom in the curvature matrix. The network is used to learn the price of derivatives such as call options and butterfly spreads on geometric baskets for $D=10,100$ achieving a time-averaged RMSE of less than $0.1$ in each case.
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