Abstract Value at Risk (VaR) and Expected Shortfall (ES) are critical metrics for quantifying financial risk. VaR estimates the maximum potential loss within a specific timeframe, while ES captures the average loss that exceeds the VaR threshold. Accurate estimation of these risk measures is vital for financial institutions; however, traditional methods often falter in addressing the dynamic volatility of financial data. This study explores the One-Factor Generalized Autoregressive Score (GAS-1F) semiparametric model, a novel approach that incorporates elicitability into its score function to circumvent distributional assumptions. Elicitability guarantees alignment between the estimated loss function and the true underlying risk measure. The GAS-1F model excels as a two-tiered, semiparametric framework for estimating VaR and ES. By applying this model to historical data from the S& P 500 index, we demonstrate its …