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

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.

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

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.

Paper Structure

This paper contains 23 sections, 2 equations, 13 figures, 1 table.

Table of Contents

  1. Introduction
  2. Motivation
  3. Literature Review
  4. Contribution
  5. Outlines
  6. FE-GAN: Architecture and Implementation
  7. Architecture of FE-GAN
  8. Test of Historical Data
  9. Test under the Assumption of GBM
  10. Test of Time Series
  11. Combination of GBM and Time Series Models
  12. Comparison between Tail-GAN and WGAN under FE-GAN
  13. Comparison Using Historical Data
  14. Comparison under the Assumption of GBM
  15. Comparison of Time Series Models
  16. ...and 8 more sections

Figures (13)

  • Figure 1: Traditional WGAN architecture.
  • Figure 2: FE-GAN architecture.
  • Figure 3: Comparison of VaR and ES differences at the 5% level between the benchmark and the test of historical data.
  • Figure 4: Comparison of VaR and ES differences at the 5% level between the benchmark and the test under the GBM assumption.
  • Figure 5: Comparison of VaR and ES differences at 5% level between the benchmark and the time series model
  • ...and 8 more figures