Gold Price Forecasting Using Hybrid ARIMA-IGARCH - Fitriani Agustina; Hasya Nur Auliya; Dadan Dasari (Universitas Muhammadiyah Metro, 2026)
Gold is an important investment and hedging instrument, with highly volatile price movements that are difficult to accurately predict. Previous studies have generally used ARIMA-GARCH models to forecast financial time series, but these models have not fully captured the persistent volatility of gold prices. Therefore, this study proposes an ARIMA-IGARCH approach to simultaneously model the mean and persistent volatility patterns of gold price movements. Daily gold closing price data from November 2022 to August 2025 was analyzed using Python, with 90% used for training and 10% for testing. The ARIMA model was used to capture the mean structure, while the IGARCH model was used to represent the long-term volatility persistence. The results showed that the proposed model achieved high forecasting accuracy, with a MAPE of 9.38%, indicating strong predictive performance. These findings indicate that the ARIMA-IGARCH model can serve as an alternative approach for gold price forecasting and financial market volatility analysis