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A Finite Mixture GARCH Approach with EM Algorithm for Energy Forecasting Applications

Author

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  • Yang Zhang

    (School of Economics & Finance Xi’an International Studies, University Xi’an, Xi’an 710049, China
    Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH 45221, USA)

  • Yidong Peng

    (Excelsior College, Albany, NY 12203, USA)

  • Xiuli Qu

    (Department of Industrial and Systems Engineering, North Carolina A&T State University, Greensboro, NC 27411, USA)

  • Jing Shi

    (Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH 45221, USA)

  • Ergin Erdem

    (Department of Engineering, Robert Morris University, Moon Township, PA 15108, USA)

Abstract

Enhancing forecasting performance in terms of both the expected mean value and variance has been a critical challenging issue for energy industry. In this paper, the novel methodology of finite mixture Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) approach with Expectation–Maximization (EM) algorithm is introduced. The applicability of this methodology is comprehensively evaluated for the forecasting of energy related time series including wind speed, wind power generation, and electricity price. Its forecasting performances are evaluated by various criteria, and also compared with those of the conventional AutoRegressive Moving-Average (ARMA) model and the less conventional ARMA-GARCH model. It is found that the proposed mixture GARCH model outperforms the other two models in terms of volatility modeling for all the energy related time series considered. This is proven to be statistically significant because the p-values of likelihood ratio test are less than 0.0001. On the other hand, in terms of estimations of mean wind speed, mean wind power output, and mean electricity price, no significant improvement from the proposed model is obtained. The results indicate that the proposed finite mixture GARCH model is a viable approach for mitigating the associated risk in energy related predictions thanks to the reduced errors on volatility modeling.

Suggested Citation

  • Yang Zhang & Yidong Peng & Xiuli Qu & Jing Shi & Ergin Erdem, 2021. "A Finite Mixture GARCH Approach with EM Algorithm for Energy Forecasting Applications," Energies, MDPI, vol. 14(9), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2352-:d:540432
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    References listed on IDEAS

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