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Main Authors: Wang, Du-Yi, Liang, Guo, Zhang, Kun, Zhu, Qianwen
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2602.01912
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author Wang, Du-Yi
Liang, Guo
Zhang, Kun
Zhu, Qianwen
author_facet Wang, Du-Yi
Liang, Guo
Zhang, Kun
Zhu, Qianwen
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2602_01912
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Reliable Real-Time Value at Risk Estimation via Quantile Regression Forest with Conformal Calibration
Wang, Du-Yi
Liang, Guo
Zhang, Kun
Zhu, Qianwen
Machine Learning
Artificial Intelligence
Risk Management
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.
title Reliable Real-Time Value at Risk Estimation via Quantile Regression Forest with Conformal Calibration
topic Machine Learning
Artificial Intelligence
Risk Management
url https://arxiv.org/abs/2602.01912