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Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

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  • Marcin Fałdziński

    (Department of Econometrics and Statistics, Faculty of Economic Sciences and Management, Nicolaus Copernicus University in Torun, ul. Gagarina 13a, 87-100 Torun, Poland
    Faculty of Finance and Accounting, Prague University of Economics and Business, W. Churchill Sq. 1938/4, Žižkov, 130 67 Prague, Czech Republic)

  • Piotr Fiszeder

    (Department of Econometrics and Statistics, Faculty of Economic Sciences and Management, Nicolaus Copernicus University in Torun, ul. Gagarina 13a, 87-100 Torun, Poland
    Faculty of Finance and Accounting, Prague University of Economics and Business, W. Churchill Sq. 1938/4, Žižkov, 130 67 Prague, Czech Republic)

  • Witold Orzeszko

    (Department of Applied Informatics and Mathematics in Economics, Faculty of Economic Sciences and Management, Nicolaus Copernicus University in Torun, ul. Gagarina 13a, 87-100 Torun, Poland)

Abstract

We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.

Suggested Citation

  • Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
  • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:6-:d:466264
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    5. Krzysztof Echaust & Małgorzata Just, 2021. "Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic," Energies, MDPI, vol. 14(14), pages 1-21, July.
    6. Kshitij Sharma & Yogesh K. Dwivedi & Bhimaraya Metri, 2024. "Incorporating causality in energy consumption forecasting using deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 537-572, August.
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