  Several well-established benchmark predictors exist for Value-at-Risk (VaR),
a major instrument for financial risk management. Hybrid methods combining
AR-GARCH filtering with skewed-$t$ residuals and the extreme value theory-based
approach are particularly recommended. This study introduces yet another VaR
predictor, G-VaR, which follows a novel methodology. Inspired by the recent
mathematical theory of sublinear expectation, G-VaR is built upon the concept
of model uncertainty, which in the present case signifies that the inherent
volatility of financial returns cannot be characterized by a single
distribution but rather by infinitely many statistical distributions. By
considering the worst scenario among these potential distributions, the G-VaR
predictor is precisely identified. Extensive experiments on both the NASDAQ
Composite Index and S\&P500 Index demonstrate the excellent performance of the
G-VaR predictor, which is superior to most existing benchmark VaR predictors.
