Abstract:
The research is based on financial time series modelling with special application to modelling inflation data for Ghana. In particular the theory of time series is explored and applied to the inflation data spanning from January 1965 to December 2012 which were obtained from the Ghana Statistical Service. Three Autoregressive Conditional Heteroscedastic (ARCH) family type models (traditional ARCH, Generalized ARCH (GARCH), and the Exponential GARCH (EGARCH)) models were fitted to the data. This was especially so because the data were characterized by changing mean and variance. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to assess the performance of each of the fitted models such that the model with the minimum value of AIC and BIC was adjudged the best model. The results revealed that the ARCH – family type models, particularly, the EGARCH (2, 1) was superior in performance in forecasting Ghana’s monthly rates of inflation. The results also showed that the monthly rates on inflation were not weakly stationary and although there was the presence of asymmetric effects in the volatility in the monthly rates of inflation, there was an absence of leverage effects as positive shock increased the volatility in the monthly rate of inflation more than a negative shock of equal magnitude. The study recommends that policy makers and all interested in modelling and forecasting monthly rates of inflation in Ghana should consider using the Heteroscedastic models as it is able to properly capture the volatilities in the monthly rates of inflation. Analysis were done using MINITAB 16.0 and EVIEWS 5.0.
