用随机森林+校准实现实时风险值的可靠估计
Reliable Real-Time Value at Risk Estimation via Quantile Regression Forest with Conformal Calibration
- 基于离线训练的分位数随机森林，在线结合风险因子实时预测
- 通过可证明可靠的校准方法，确保估计结果覆盖真实值
- 适合金融风控场景，尤其需要高可靠性实时决策的系统
快速变化的市场环境要求实时风险监控，但在线估计仍具挑战。本文研究最广泛应用的风险度量之一——风险价值（VaR）的在线估计。准确可靠的估计对及时风险控制和决策至关重要。我们提出在离线-仿真-在线-估计（OSOA）框架下使用分位数回归森林。具体而言，分位数回归森林离线学习在线VaR与风险因子的关系，随后在线阶段结合观测到的风险因子生成实时估计。为进一步保障可靠性，我们开发了一种通过分位数校准的估计器来校准在线估计结果。据我们所知，这是首个基于OSOA框架，利用分位数校准实现可靠实时VaR估计的工作。理论分析证明了所提估计器的一致性和覆盖率有效性。数值实验验证了方法的有效性，并展示了其实际应用效果。
原文摘要 · Abstract (English)
Rapidly evolving market conditions call for real-time risk monitoring, but its online estimation remains challenging. In this paper, we study the online estimation of one of the most widely used risk measures, Value at Risk (VaR). Its accurate and reliable estimation is essential for timely risk control and informed decision-making. We propose to use the quantile regression forest in the offline-simulation-online-estimation (OSOA) framework. Specifically, the quantile regression forest is trained offline to learn the relationship between the online VaR and risk factors, and real-time VaR estimates are then produced online by incorporating observed risk factors. To further ensure reliability, we develop a conformalized estimator that calibrates the online VaR estimates. To the best of our knowledge, we are the first to leverage conformal calibration to estimate real-time VaR reliably based on the OSOA formulation. Theoretical analysis establishes the consistency and coverage validity of the proposed estimators. Numerical experiments confirm the proposed method and demonstrate its effectiveness in practice.
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