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AUTHOR(S): Tirngo Dinku
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ABSTRACT This study aimed to model and forecast the volatility of returns for selected agricultural commodities prices using generalized autoregressive conditional heteroskedasticity GARCH models in Ethiopia. GARCH family models, specifically GARCH, threshold generalized autoregressive conditional heteroskedasticity TGARCH, and exponential generalized autoregressive conditional heteroskedasticity EGARCH were employed to analyze the timevarying volatility of selected agricultural commodities prices from 2011to 2021. The data analysis results revealed that, out of the GARCH specifications, the TGARCH model with the Normal distributional assumption of residuals was a better fit model for the price volatility of Teff and Red Pepper in which their return series reacted differently to the good and the bad news. The study indicated the existence of leverage effect, which implied that the bad news could have a larger effect on volatility than the good news of the same magnitude, and the asymmetric term was significant. Also, the TGARCH model was found to be the accurate model for forecasting price return volatility of the same commodities, namely Teff and Red Pepper. In short, the study concludes that TGARCH was to be the best fit to model and forecast price return volatility of Teff and Red Pepper in the Ethiopian context |
KEYWORDS Price volatility, Agricultural commodities, GARCH, TGARCH, EGARCH |
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Cite this paper Tirngo Dinku. (2021) Modeling Price Volatility for Selected Agricultural Commodities in Ethiopia: The Application of GARCH Models. International Journal of Environmental Science, 6, 264-277 |
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