PERFORMA PREDIKSI: KOINTEGRASI GARCH-SVR VS. GARCH UNTUK VOLATILITAS HARGA KOMODITAS ENERGI GLOBAL - Prajna Pramita Izati; Fariz Budi Arafat (Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, 2025)
Forecasting provides benefits in decision-making, one of which is forecasting the volatility of global energy commodity prices. However, there are challenges in forecasting volatility due to the presence of heteroskedasticity and long-memory effects in the data. Therefore, a combination of the GARCH and SVR methods is needed as a cointegration-based machine learning approach. The aim of this study is to compare the forecasting performance of GARCH and GARCH-SVR for global energy commodity price volatility. The findings indicate that the GARCH-SVR model performs well when volatility data exhibits non-stationary long-memory characteristics, whereas the GARCH model is more suitable when the volatility data shows stationary long-memory characteristics.