Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality

TL;DR

This paper demonstrates that random feature neural networks can effectively learn solutions to Black-Scholes type PDEs without suffering from the curse of dimensionality, supported by theoretical bounds and numerical validation.

Contribution

It provides the first theoretical analysis showing random feature neural networks can learn Black-Scholes PDEs efficiently regardless of dimension.

Findings

Error bounds do not suffer from curse of dimensionality.

Neural networks successfully learn solutions to Black-Scholes PDEs.

Numerical validation confirms theoretical predictions.

Abstract

This article investigates the use of random feature neural networks for learning Kolmogorov partial (integro-)differential equations associated to Black-Scholes and more general exponential L\'evy models. Random feature neural networks are single-hidden-layer feedforward neural networks in which only the output weights are trainable. This makes training particularly simple, but (a priori) reduces expressivity. Interestingly, this is not the case for Black-Scholes type PDEs, as we show here. We derive bounds for the prediction error of random neural networks for learning sufficiently non-degenerate Black-Scholes type models. A full error analysis is provided and it is shown that the derived bounds do not suffer from the curse of dimensionality. We also investigate an application of these results to basket options and validate the bounds numerically. These results prove that neural networks are able to \textit{learn} solutions to Black-Scholes type PDEs without the curse of dimensionality. In addition, this provides an example of a relevant learning problem in which random feature neural networks are provably efficient.