Risk Management with Feature-Enriched Generative Adversarial Networks (FE-GAN)

Paper: arXiv 2411.15519

Authors: Ling Chen

Abstract

This paper investigates the application of Feature-Enriched Generative Adversarial Networks (FE-GAN) in financial risk management, with a focus on improving the estimation of Value at Risk (VaR) and Expected Shortfall (ES). FE-GAN enhances existing GANs architectures by incorporating an additional input sequence derived from preceding data to improve model performance. Two specialized GANs models, the Wasserstein Generative Adversarial Network (WGAN) and the Tail Generative Adversarial Network (Tail-GAN), were evaluated under the FE-GAN framework. The results demonstrate that FE-GAN significantly outperforms traditional architectures in both VaR and ES estimation. Tail-GAN, leveraging its task-specific loss function, consistently outperforms WGAN in ES estimation, while both models exhibit similar performance in VaR estimation. Despite these promising results, the study acknowledges limitations, including reliance on highly correlated temporal data and restricted applicability to other domains. Future research directions include exploring alternative input generation methods, dynamic forecasting models, and advanced neural network architectures to further enhance GANs-based financial risk estimation.

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a novel feature-enrichment architecture (FE-GAN) for improving generative models in tail-risk estimation. It demonstrates practical utility on VIX data across multiple baseline models, combining standard quantitative financial models with modern deep learning techniques.

Research Flowchart

  flowchart TD
    A[Research Goal: Improve VaR & ES Estimation in Financial Risk] --> B[Input Data: Historical Temporal Data & Derived Feature Sequences]
    B --> C[FE-GAN Framework: Incorporate Preceding Data Inputs]
    C --> D[Model Training: Evaluate WGAN vs Task-Specific Tail-GAN]
    D --> E[Key Finding 1: FE-GAN Outperforms Traditional GAN Architectures]
    D --> F[Key Finding 2: Tail-GAN Superior for ES; Both Equal for VaR]