Modeling and Forecasting the Macroeconomic Driver of Time-Varying Coffee Price Volatility in Ethiopia

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Abstract Recently, modeling and forecasting of high frequency data (such as daily price) volatility using GARCH-MIDAS attracts the attention of many researchers. Thus, the objective of this study is to model and forecast the average daily coffee price volatility using GARCH-MIDAS model over the sample period from January 01, 2010 to June 30, 2019. The GARCH-MIDAS component model decomposes the conditional variance into short run component which follows a mean-reverting unit GARCH process and long run component which consider different frequency macroeconomic indicators (in this study GDP, interest rate, trade openness and money supply) via mixed interval data sampling (MIDAS) specification. Unit root test results show the return series are stationary at level, while macroeconomic variables are stationary at first difference except interest rate, which is stationary at level. From the result of estimated GARCH-MIDAS component model, all selected indicators are crucial in explaining the long-term component price volatility.The Conrad &Schienleregression based result shows the absence of model specification. Moreover, the estimated GARCH-MIDAS component model with money supply as a main driver is used for out-sample forecast. Finally, the DM test statistic used for comparing the forecasting performance of GARCH-MIDAS component model against the standard GARCH model.The result shows that multiplicative GARCH-MIDAS component model provide an explanation for stylized facts that cannot be captured by standard GARCH model.

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