Can GANs Learn the Stylized Facts of Financial Time Series?

TL;DR

This paper investigates whether GANs can effectively learn the complex stylized facts of financial time series, revealing that their success depends heavily on the generator architecture used.

Contribution

It provides an empirical evaluation of GANs' ability to model financial time series' stylized facts, emphasizing the importance of architecture selection.

Findings

GANs can capture stylized facts of financial data

Performance varies with generator architecture

Careful validation is crucial for effective modeling

Abstract

In the financial sector, a sophisticated financial time series simulator is essential for evaluating financial products and investment strategies. Traditional back-testing methods have mainly relied on historical data-driven approaches or mathematical model-driven approaches, such as various stochastic processes. However, in the current era of AI, data-driven approaches, where models learn the intrinsic characteristics of data directly, have emerged as promising techniques. Generative Adversarial Networks (GANs) have surfaced as promising generative models, capturing data distributions through adversarial learning. Financial time series, characterized 'stylized facts' such as random walks, mean-reverting patterns, unexpected jumps, and time-varying volatility, present significant challenges for deep neural networks to learn their intrinsic characteristics. This study examines the ability of GANs to learn diverse and complex temporal patterns (i.e., stylized facts) of both univariate and multivariate financial time series. Our extensive experiments revealed that GANs can capture various stylized facts of financial time series, but their performance varies significantly depending on the choice of generator architecture. This suggests that naively applying GANs might not effectively capture the intricate characteristics inherent in financial time series, highlighting the importance of carefully considering and validating the modeling choices.