Properly managing the supply and demand for liquidity of banks, as the largest financial institution in the money market, by reducing deposits and other liabilities alongside the growth of a portfolio of loans, other assets, and off-balance sheet items is a challenging issue in banking risk management. Therefore, to continue banking activities and avoid the risks of lack of liquidity, liquidity management evaluates and controls liquidity risk. For this purpose, in the present study, it was attempted to design an appropriate model for predicting liquidity risk based on a parametric approach to control efficient and effective liquidity risk management. Accordingly, the liquidity at risk was calculated using the family of autoregressive conditional heteroskedasticity models. We used the daily data and facilities of a private bank in the period between September 23^rd, 2010, to September 22^nd, 2019, based on the concept of value at risk. The results of the evaluation of the utilized models, used by backtest, confirm the parametric approach in predicting daily bank risk liquidity.