Reliable Real-Time Value at Risk Estimation via Quantile Regression Forest with Conformal Calibration

2026-02-02

AI Summary

This study addresses the challenge of reliably estimating real-time Value-at-Risk (VaR) in financial markets by proposing a novel approach that integrates an offline simulation–online estimation (OSOA) framework, quantile regression forests, and conformal calibration. For the first time, conformal prediction is incorporated into the OSOA framework to model the nonlinear relationship between VaR and risk factors while calibrating online estimates to ensure theoretically guaranteed coverage validity and statistical consistency. Theoretical analysis establishes the consistency of the proposed estimator and provides finite-sample coverage guarantees. Extensive numerical experiments further demonstrate the method’s computational efficiency and robustness in practical financial settings.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Value at Risk
• real-time estimation
• online risk monitoring
• quantile regression
• conformal calibration

Innovation

• Quantile Regression Forest
• Conformal Calibration
• Value at Risk
• Online Estimation
• OSOA Framework