Application of Autoregressive Conditional Heteroskedasticity (ARCH) Type Models for the Price Volatility of Teff in Ethiopia - Denano, Tesfaye; Sibera, Sintayehu (International Journal of Applied Sciences: Current and Future Research Trends, 2021)
The main aim of this study was to forecast the monthly price volatility of Teff in Ethiopia. The dataset was obtained from the central statistical agency which indexes from January 2014 to June 2021. Thus, ARIMA family models for the mean equation and GARCH family models for the variance equation were employed to forecast the monthly price volatility of Teff. Among the GARCH family models considered in this study, the ARIMA (1,1, 1)-EGARCH (1, 1) model with student t-distributional assumption of residuals was found to be a better fit for the price volatility of Teff. The exchange rate, food inflation rate, non-food inflation rate and fuel oil were found to have a statistically significant effect on the average monthly price volatility of Teff. The asymmetric term was found to be positive and statistically significant in the EGARCH (1, 1) volatility model for Teff. This is an indication that the unanticipated increase in domestic prices had a larger impact on domestic price volatility than the unanticipated decrease in the domestic price of Teff. Finally, various forecast accuracy measurement statistics indicate that the estimated ARIMA (1,1,1) model is good enough to describe the domestic price of Teff. Moreover, the out-of-sample forecast indicates that the domestic price of Teff is increasing. The in-sample forecast using the best-fit asymmetric model, which is the EGARCH (1, 1) model, indicates that the domestic price volatility of Teff remained at almost a constant level around the beginning and end of the study. But it increased steadily at an increasing rate from December 2019 to January 2021, then dropped sharply at an increasing rate till March 2021.