The present study suggests a model for predicting liquidity gap, based on source and cost of funds approach concerning the daily time series data (25 March 2009 to 19 March 2018), in order to control and manage the liquidity risk. Using the family of autoregressive conditional heteroscedasticity models, the behavior of bank liquidity gap is modeled and predicted. The results show that the APGARCH with the Johnson-SU distribution is the most suitable model for explaining the liquidity gap behavior. Based on the rolling window method the more accurate model has been selected to be the APGARCH model with T-Student distribution which provides the least error in forecasting liquidity gap.
