Tackling the Exponential Scaling of Signature-Based Generative Adversarial Networks for High-Dimensional Financial Time-Series Generation

Abstract

The authors propose a framework to simulate high-dimensional financial time series based on generative adversarial networks (GANs).n The resulting implementation solves the exponential scaling problem of standard GANs by approximating their correlation structure with a hierarchical clustering.n Key metrics show the generated data to have more variation than benchmark approaches.