Median-Anchored Adjustment of Joint VaR--ES Forecasts

Tae-Hwy Lee () and Dingli Wang ()
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Tae-Hwy Lee: Department of Economics, University of California Riverside
Dingli Wang: University of California, Riverside

No 202604, Working Papers from University of California at Riverside, Department of Economics

Abstract: Risk managers often work with a fitted forecasting model they cannot replace even when its tail forecasts need adjustment. We propose a median-anchored rule that multiplies the distances from the fitted median to Value-at-Risk (VaR) and Expected Shortfall (ES) by a common positive multiplier. The rule preserves VaR--ES ordering, and minimizing VaR check loss gives a closed-form weighted-quantile estimator. We establish consistency, give an asymptotic distribution under high-level conditions accounting for baseline estimation, and derive a VaR coverage-error bound at the forecast origin. When the same multiplier correctly adjusts both VaR and ES, the adjusted pair has lower conditional zero-homogeneous Fissler--Ziegel (FZ0) risk at that origin. Monte Carlo experiments examine this condition and several forms of misspecification. In rolling S&P~500 forecasts the adjustment lowers FZ0 loss in all four GARCH cells, each pairwise significant, with the largest and most robust gain, 10.4%, for Gaussian GARCH at the 1% tail; the 5% gains do not survive the most conservative family-wise adjustments. A quantile-regression baseline marks the boundary: when the fitted tail is already flexible, one multiplier adds little. Filtered historical simulation quantifies what standardized residuals and conditional scales add when available.

Keywords: Backtesting; Elicitability; Fissler--Ziegel Score; Forecast Evaluation; Model Risk; Quantile Regression (search for similar items in EconPapers)
Pages: 81 Pages
Date: 2026-08
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