Taming Tail Risk in Financial Markets: Conformal Risk Control for Nonstationary Portfolio VaR

TL;DR

This paper introduces a regime-weighted conformal risk control method for nonstationary portfolio VaR, improving risk forecasting stability across changing market regimes by calibrating safety buffers with past errors and regime features.

Contribution

It proposes a novel regime-weighted conformal calibration technique that adapts to nonstationary market regimes, enhancing VaR control in dynamic financial environments.

Findings

Time-weighted conformal calibration performs well under market drift.

Regime weighting improves stability in certain market conditions.

Finite-sample coverage is theoretically established.

Abstract

Risk forecasts drive trading constraints and capital allocation, yet losses are nonstationary and regime-dependent. This paper studies sequential one-sided VaR control via conformal calibration. I propose regime-weighted conformal risk control (RWC), which calibrates a safety buffer from past forecast errors using exponential time decay and regime-similarity weights from regime features. RWC is model-agnostic and wraps any conditional quantile forecaster to target a desired exceedance rate. Finite-sample coverage is established under weighted exchangeability, and approximation bounds are derived under smoothly drifting regimes. On the CRSP U.S.\ equity portfolio, time-weighted conformal calibration is a strong default under drift, while regime weighting can improve regime-conditional stability in some settings with modest conservativeness changes.