用增强特征的GAN提升金融风险预测准确率
Risk Management with Feature-Enriched Generative Adversarial Networks (FE-GAN)
- 引入历史数据序列增强GAN，提升风险建模能力
- 尾部GAN在预期损失估计上显著优于传统方法
- 适合金融风控、量化交易等需要精准风险评估的场景
本文研究了特征增强生成对抗网络（FE-GAN）在金融风险管控中的应用，重点改进风险价值（VaR）和预期损失（ES）的估计。FE-GAN通过引入前序数据构成的额外输入序列，优化现有GAN架构性能。在FE-GAN框架下，评估了两种专用GAN模型：Wasserstein GAN（WGAN）与尾部GAN（Tail-GAN）。结果表明，FE-GAN在VaR与ES估计上均显著优于传统架构。尾部GAN凭借其任务特异性损失函数，在预期损失估计中持续领先于WGAN，而两者在风险价值估计上表现相近。尽管结果乐观，研究仍指出其依赖高度相关的时间序列数据，且适用范围受限。未来方向包括探索替代输入生成方式、动态预测模型及更先进的神经网络结构，以进一步提升基于GAN的金融风险估计能力。
原文摘要 · Abstract (English)
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.
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