用强化学习动态调整风险预测，提升金融风控准确性。
Bridging Econometrics and AI: VaR Estimation via Reinforcement Learning and GARCH Models
- 融合GARCH与双深度Q网络，实现市场方向预测
- 在欧股50指数上降低违规次数和资本要求
- 适合需要实时风控的金融机构使用
在金融市场波动加剧的背景下，准确估计风险仍是重大挑战。传统计量经济模型（如GARCH及其变体）依赖过强假设，难以适应当前市场复杂性。为此，本文提出一种混合框架，结合GARCH波动率模型与深度强化学习，用于价值风险（VaR）估计。该方法采用双深度Q网络（DDQN）进行市场方向预测，将任务视为不平衡分类问题，使风险预测可随市场状况动态调整。基于涵盖危机与高波动期的欧元斯托克50日度数据的实证验证表明，该方法显著提升了VaR估计精度，减少了违约次数与资本需求，同时满足监管风险阈值要求。模型实时调参能力增强了其在现代主动风险管理中的应用价值。
原文摘要 · Abstract (English)
In an environment of increasingly volatile financial markets, the accurate estimation of risk remains a major challenge. Traditional econometric models, such as GARCH and its variants, are based on assumptions that are often too rigid to adapt to the complexity of the current market dynamics. To overcome these limitations, we propose a hybrid framework for Value-at-Risk (VaR) estimation, combining GARCH volatility models with deep reinforcement learning. Our approach incorporates directional market forecasting using the Double Deep Q-Network (DDQN) model, treating the task as an imbalanced classification problem. This architecture enables the dynamic adjustment of risk-level forecasts according to market conditions. Empirical validation on daily Eurostoxx 50 data covering periods of crisis and high volatility shows a significant improvement in the accuracy of VaR estimates, as well as a reduction in the number of breaches and also in capital requirements, while respecting regulatory risk thresholds. The ability of the model to adjust risk levels in real time reinforces its relevance to modern and proactive risk management.
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