GARCH As An Inflation Prediction Tool: An Empirical Study Of Indonesian Economic Data

Mardi Nurul Anto, Syaharuddin Syaharuddin, Vera Mandailina

Abstract


Abstract: This study is important because inflation volatility has a significant impact on macroeconomic stability and monetary policy decisions in Indonesia. Therefore, the objective of this research is to evaluate the performance of the GARCH (1,1) model in forecasting inflation volatility and analyzing its dynamic behavior. This study is an experiment aimed at forecasting inflation data for the next five years based on actual data from the 2015–2024 period. The data were obtained from Statistics Indonesia (BPS) and Bank Indonesia (BI). The results show that the GARCH (1,1) model can adequately capture the trend of volatility, as indicated by a Mean Squared Error (MSE) of 0.566398. However, the model's accuracy in predicting monthly fluctuations remains low, with a Mean Absolute Percentage Error (MAPE) of 99.19%. The implications of these findings suggest that while the GARCH (1,1) model is useful for identifying long-term volatility trends, further improvements—such as exploring advanced GARCH variants and incorporating additional macroeconomic variables—are necessary to support the formulation of more effective inflation control policies.

Keywords


Inflation, Volatility, GARCH (1,1), Forecasting, Indonesia, Time Series.

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References


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