Modeling and forecasting of volatile data have become the area of interest in _nancial timernseries.This study was conducted to apply a hybrid model between Autoregressive IntegratedrnMoving Average (ARIMA) model and Generalized Autoregressive Conditional Hetero-rncedasticity (GARCH) family model. Symmetric (GARCH) and asymmetric(EGARCH)rnmodels are used in this study.The time series data used in this study consist of averagernmonthly Ethiopian Birr/ USA Dollar exchange rate from January 2001 to February 2020rnobtained from National bank of Ethiopia.The data were converted to returns to enhancerntheir statistical properties and the returns was used to _t a mean and the variance equa-rntion.The parameters for ARIMA models were estimated using Ordinary Least SquaresrnEstimation (OLS) method. For hybrid ARIMA-GARCH and ARIMA-EGARCH, the pa-rnrameters were estimated by using Maximum Likelihood Estimation (MLE).The ARCHrnLM test indicate that presence of conditional heteroscedasticity in the ARIMA model.rnThe performance of modeling and forecasting of hybrid ARIMA- GARCH type modelsrnhave been investigated based on forecasting performance criteria such as MSE, MAE andrnMAPE tests. The data are divided into two parts where 89% of the data is used as in-rnsample((build the model) period taken from January 2001 until January 2018. while thernrest of data (11%) were used for the out-sample period taken from February 2018 untilrnFebruary 2020.The modeling performance of the hybrid models are evaluated using AIC.rnResults showed that,hybrid ARIMA (3,1,3)-EGARCH(3,1) model was found to be performrnbetter in modeling and forecasting the volatility of monthly exchange rate return comparedrnto hybrid ARIMA(3,1,3)-GARCH (1,1) model. The processes of modeling and forecastingrnwas done by using Eviews statistical software.