Before revision:  By utilizing the value at risk (VaR) predictor model under volatility uncertainty, we introduce the risk and uncertainty, and show that the autoregressive model of volatility uncertainty is a powerful tool in predicting the VaR for a benchmark dataset  .
After revision:  By utilizing the value at risk (VaR) predictor model under G-normal distribution, we show that the autoregressive model of volatility uncertainty is a powerful tool in predicting the VaR for a benchmark dataset .