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The study was on Generalized Autoregressive Conditional Heteroscedastic (GARCH) and Seasonal Autoregressive Integrated Moving Average (SARIMA) Modeling of Nigerian Narrow Money. It was aimed at examining the behavior of the monthly data of Nigerian Narrow Money from January 2000 to February 2018 and to construct a suitable GARCH model for the series. Information retrieved from the CBN website in Nigeria. A non-constant volatility was evident in the data, as is typical of financial time series. We found and applied the Generalized Autoregressive Conditional Heteroscedastic GARCH and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to analyze the time series. The findings demonstrated that compared to the GARCH(1,1) model, the SARIMA model had a larger AIC. Since the GARCH(1,1) model provided a lower Akaike Information Criterion (AIC), it is superior for this series.