Peramalan Harga Emas Berjangka Menggunakan Metode ARIMA-GARCH - Hasanah, Mauizatun; Putri, Mega Ramatika; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul (Universitas Negeri Gorontalo, 2025)
Gold futures price forecasting plays an important role in investment decision-making and risk management, especially in the midst of volatile commodity market dynamics. This research aims to build an accurate gold futures price forecasting model by combining Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The ARIMA model is used to capture linear patterns and historical trends in time series data, while the GARCH model is able to handle the high volatility characteristic of gold price movements, something that conventional forecasting models often fail to capture. The data used in this study is daily gold futures price data collected over the period January 3, 2023 to March 31, 2025, which covers both normal market conditions and periods of turmoil, making it relevant to describe the overall market dynamics. The forecasting results show that the ARIMA-GARCH model with components (3,1,3) (1,1) with a MAPE of 4.52% indicates a good level of accuracy in the context of forecasting gold futures prices that have high volatility. Thus, this model provides precise forecasting results with actual data so that it can be used by market participants and policy makers in managing risks and designing strategies.