Abstract
ABSTRACT Recently, modeling and forecasting of high-frequency data (such as daily price) volatility using GARCH-MIDAS attract the attention of many researchers. Thus, the objective of this study is to model the average daily coffee price volatility from 1 January 2010 to 30 June 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 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 model, all selected indicators are crucial in explaining price volatility. . Moreover, the estimated GARCH-MIDAS model with money supply as a main driver is used for out-sample forecast. Based on, DM test statistic multiplicative GARCH-MIDAS model provides an explanation for stylized facts that cannot be captured by standard GARCH model.
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