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.

翻译：本文研究了特征增强生成对抗网络（FE-GAN）在金融风险管理中的应用，重点在于改进在险价值（VaR）与预期缺口（ES）的估计。FE-GAN通过引入一个源自先前数据的额外输入序列来提升模型性能，从而增强了现有生成对抗网络（GAN）的架构。在FE-GAN框架下，评估了两种专门的GAN模型：Wasserstein生成对抗网络（WGAN）与尾部生成对抗网络（Tail-GAN）。结果表明，FE-GAN在VaR和ES估计上均显著优于传统架构。Tail-GAN利用其任务特定的损失函数，在ES估计中持续优于WGAN，而两种模型在VaR估计中表现出相近的性能。尽管取得了这些有希望的结果，本研究也承认了其局限性，包括对高度相关的时间序列数据的依赖以及在其他领域应用受限。未来的研究方向包括探索替代的输入生成方法、动态预测模型以及先进的神经网络架构，以进一步提升基于GAN的金融风险估计能力。