Can GANs Learn the Stylized Facts of Financial Time Series?

ArXiv ID: 2410.09850 “View on arXiv”

Authors: Unknown

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.

Keywords: Generative Adversarial Networks, GANs, financial time series simulation, stylized facts, deep learning, Financial Time Series

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper presents advanced mathematical concepts including stochastic processes (Ornstein-Uhlenbeck, Heston, Jump Diffusion) and KL/Jensen-Shannon divergence, but it is also heavily empirical with extensive experiments on multiple GAN architectures, hyperparameter optimization, and specific metrics for financial stylized facts.

  flowchart TD
    A["Research Goal: Assess GANs' ability to learn stylized facts of financial time series"] --> B["Data Preparation"]
    B --> B1["Univariate & Multivariate Financial Time Series"]
    B --> B2["Identify Stylized Facts<br/>Random Walk, Mean Reversion, Jumps, Volatility"]
    B --> C["Methodology: GAN Architecture Comparison"]
    C --> D["Generative Process<br/>Generator vs Discriminator<br/>Adversarial Training"]
    D --> E{"Evaluation of Generated Series"}
    E -->|Capture Stylized Facts?| F["Key Findings"]
    E -->|Inadequate| G["Refine Model / Select Architecture"]
    G --> D
    F --> F1["GANs capture stylized facts but performance varies"]
    F --> F2["Generator architecture is critical"]
    F --> F3["Naive application is insufficient for complex time series"]