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

TL;DR

This paper introduces a novel method combining quantile regression forests and conformal calibration within an OSOA framework to provide reliable, real-time Value at Risk estimates in dynamic markets.

Contribution

It is the first to apply conformal calibration to real-time VaR estimation using quantile regression forests in an OSOA setting, ensuring reliability and validity.

Findings

The proposed method achieves consistent VaR estimates.

It provides valid coverage guarantees.

Numerical experiments demonstrate practical effectiveness.

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.