DOI: https://doi.org/10.14710/j.gauss.14.2.500-511

PERFORMA PREDIKSI: KOINTEGRASI GARCH-SVR VS. GARCH UNTUK VOLATILITAS HARGA KOMODITAS ENERGI GLOBAL

*Prajna Pramita Izati  -  Department of Statistics, Universitas Diponegoro, Jl. Prof. Sudarto SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Fariz Budi Arafat  -  Department of Statistics, Universitas Diponegoro, Jl. Prof. Sudarto SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Open Access Copyright 2025 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
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.

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
Similiarity
Subject
Type Research Instrument
  Download (2MB)    Indexing metadata
Keywords: GARCH; GARCH-SVR; Long memory; SVR; Volatility

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
Front Matter ANALISIS TINGKAT PENGANGGURAN TERBUKA BERDASARKAN INDIKATOR SOSIAL DI PROVINSI KEPULAUAN BANGKA BELITUNG MENGGUNAKAN PENDEKATAN REGRESI B-SPLINE IMPLEMENTASI METODE SUPPORT VECTOR REGRESSION UNTUK PREDIKSI HARGA SAHAM PT. ADARO ENERGY TBK PEMODELAN JUMLAH KASUS KEMATIAN BAYI DAN IBU DI PROVINSI LAMPUNG MENGGUNAKAN BIVARIATE GENERALIZED POISSON REGRESSION More recent articles
  1. Abdullah, A., Kuswanto, H., Prastyo, D. & Suhartono, 2020. Modeling the volatility of gross domestic product based on the volatility of money supply, inflation, interest rate, and exchange rate using the fractional cointegration model. Journal of Physics: Conference Series
  2. Ariefianto, M. D., 2012. Ekonometrika : Esensi dan aplikasi dengan menggunakan (Pertama ed.). Jakarta: Penerbit Erlangga
  3. Bakshi, S. S., Jaiswal, R. K. & Jaiswal, R., 2021. Efficiency Check Using Cointegration and Machine learning approach: Crude Oil Futures Markets. Procedia Computer Science, pp. 304-311
  4. Bollerslev, T., 1986. Generalized Autoregressive COnditional Heterocedasticity. Journal of econometrics, Volume 31, pp. 307-327
  5. Caporin, M., 2002. FIGARCH models: stationarity, estimation methods and the identification problem. Greta Working Paper, pp. 1-58
  6. Charles, A. & Darne, O., 2017. Forecasting crude-oil market volatility: Further evidence with jumps. Energy Economics, Volume 67, pp. 508-519
  7. Chuanhe, S., Zhongwen, L. & Ying, L., 2013. Nonlinear Cointegration Analysis Based on Support Vector Machines and Its Application to the Connection between Financial Markets. Computer Science and Engineering, pp. 117-121
  8. Cortes, C. & Vapnik, V., 1995. Support-vector networks. Mach. Learn., 20(3), p. 273–297
  9. Datong, L., Yu, P. & Xiyuan, P., 2009. Fault Prediction based on Time Series with Online Combined Kernel SVR Methods. Singapore, s.n
  10. Engle, R., 1982. Autoregressive Conditional Heterocedasticity with Estimates of Variance of UK Infaltion. Econometrica, pp. 987-1008
  11. Granger, C. J., 1981. "Some Properties of Time Series Data and Their Use in Econometric Model Specification. Journal of Econometrics,, pp. 121-130. Gujarati, D., 2004. Basic Econometrics (4th ed.). New York: The McGraw-Hill
  12. Lin, B. & Li, J., 2015. The spillover effect across natural gas and oil markets: Based on the VEC-MGRACH framework. Applied Energy, 155(6), pp. 229-241
  13. Lu, C. J., 2009. Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, Volume 47, pp. 115-125
  14. Mandelbrot, B. B., 1963. The Variation of Certain Speculative Prices. The Journal of Business, 36(4), pp. 394-419
  15. Pindyck, R., 2004. Volatility in natural gas and oil markets, Cambridge: Massachusetts Institute of Technology
  16. Ramirez, J. A., Soriano, A., Myriam, C. & Rodolfo, S., 2003. Evaluation of a mining project under the joint effect of commodity price and exchange rate uncertainties using real options valuation. Physica A: Statistical Mechanics and Its Applications , 332(C), pp. 583-596
  17. Wei, W., 2006. Time Series Analysis Univariate and Multivariate Methods, Second Edition. New York: Temple University

Last update:

No citation recorded.

Last update:

No citation recorded.