Penerapan Metode Generalized Auto-Regressive Conditional Heteroscedasticity untuk Peramalan Harga Minyak Mentah Dunia

Authors

  • Putri Zainal Department of Statistics, IPB University, Indonesia
  • Yenni Angraini Department of Statistics, IPB University, Indonesia
  • Akbar Rizki Department of Statistics, IPB University, Indonesia

DOI:

https://doi.org/10.29244/xplore.v12i1.1096

Keywords:

ARIMA, ARCH, crude oil price, forecast, GARCH

Abstract

Crude oil is one of the commodities that are needed in various fields. World crude oil prices that continue to fluctuate, of course, have a big influence on the country's economy. Crude oil price data collected is time series or the collection process is carried out from time to time with monthly periods. Therefore, we need a system that can forecast future world crude oil prices which are expected to be taken into consideration by the government for decision making. One method that can be used to predict world crude oil prices is ARIMA (Auto-Regressive Integrated Moving Average) and GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) model. After modeling, it is proven that the world crude oil price data for the period January 2002 to June 2022 has a heteroscedasticity effect that cannot be overcome if only using the ARIMA model. The results of data processing show that the ARIMA (0,1,2) followed by the ARCH (2) is the best model with a MAPE value of 5,32%. The accuracy values obtained are classifield as very good for forecasting world crude oil prices.

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Published

2023-01-15

How to Cite

Zainal, P., Angraini, Y., & Rizki, A. (2023). Penerapan Metode Generalized Auto-Regressive Conditional Heteroscedasticity untuk Peramalan Harga Minyak Mentah Dunia. Xplore: Journal of Statistics, 12(1), 12–21. https://doi.org/10.29244/xplore.v12i1.1096