TITLE:
Modeling and Forecasting of Ghana’s Inflation Volatility
AUTHORS:
Abdul-Karim Iddrisu, Dominic Otoo, Iddrisu Wahab Abdul, Sylvia Ankamah
KEYWORDS:
Exponential Generalized Auto-Regressive Conditionally Heteroskedasticity, Generalized Auto-Regressive Conditional Heteroskedasticity Model, Macroeconomic
JOURNAL NAME:
American Journal of Industrial and Business Management,
Vol.9 No.4,
April
26,
2019
ABSTRACT:
In this paper, we assessed volatility of Ghana’s inflation rates for 2000 to 2018 using the auto-regressive conditionally heteroskedasticity (ARCH), generalized ARCH (GARCH), and the exponential GARCH (EGARCH) models. The inflation data were obtained from the Ghana Statistical Service (GSS). The proposed model should be able to provide projections of inflation volatility from 2019 and beyond. The results showed that higher order models are required to properly explain Ghana’s inflation volatility and the EGARCH(12, 1) is the best fitting model for the data. The EGARCH(12, 1) model is robust to model and forecast volatility of inflation rates. Also, the results suggest that we are forecasting increasing volatility and there is increasing trend in general prices of goods and services for 2018 and beyond. The forecasts figures revealed that Ghana’s economy is likely to be unstable in 2018 and 2019. This study therefore recommends that policy makers and industry players need to put in place stringent monetary and fiscal policies that would put the anticipated increase in inflation under control. The models were implemented using R software.