APPLICATION OF GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY (GARCH) MODEL IN FORECASTING THE MARKET PRICE OF NICKEL IN INDONESIA - Sidjara, Sahlan; Sanusi, Wahidah; Nyulle, Rusdianto (Journal of Mathematics, Computations and Statistics, 2025)
Indonesia is one of the largest nickel exporting countries in the world, with the increasing demand for electric vehicles making nickel a target for producers. The increase in nickel demand makes it necessary to increase the observation of nickel prices to maintain the sustainability of the mining industry and economic growth. The purpose of this study is to forecast the price of Indonesia's nickel market using the GARCH method. The GARCH method is one of the methods used in time series data modeling that identifies heteroscedatic effects. The steps taken are to analyze the training data, check the stationery, estimate the parameters, and test the diagnostic model, then the best ARIMA model is selected based on the smallest AIC value, namely ARIMA (0,1,1). The residual values of the best ARIMA models are then used to determine the GARCH model. The best GARCH model obtained is GARCH (0.1) with an AIC value of 19.04061. Furthermore, forecasting was carried out using the GARCH model (0.1) and comparing the forecast results with the testing data to obtain MAPE values. The MAPE value obtained is 17.67014 % which shows that the GARCH model (0.1) has good forecasting accuracy, so this model is quite feasible to be used in forecasting the price of Indonesia's nickel market.