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Day-ahead electricity price forecasting with emphasis on its volatility in Iran (GARCH combined with ARIMA models)

Author

Listed:
  • Pourghorban, Mojtaba
  • Mamipour, Siab

Abstract

This paper provides a method to forecast day-ahead electricity prices based on autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedastic (GARCH) models. In the competitive power market environment, electricity price forecasting is an essential task for market participants. However, time series of electricity price has complex behavior such as nonlinearity, nonstationarity, and high volatility. ARIMA is suitable in forecasting, but it is not able to handle nonlinearity and volatility are existent in time series. Therefore, GARCH models are used to handle volatility in the in time series forecasting. The proposed method is computed using the daily electricity price data of Iran market for a five-year period from March 2013 to February 2018. The results reported in this paper illustrate the potential of the proposed ARMA-GARCH model and this combined model has been successfully applied to real prices in the Iranian power market.

Suggested Citation

  • Pourghorban, Mojtaba & Mamipour, Siab, 2019. "Day-ahead electricity price forecasting with emphasis on its volatility in Iran (GARCH combined with ARIMA models)," MPRA Paper 94826, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:94826
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    File URL: https://mpra.ub.uni-muenchen.de/94826/1/MPRA_paper_94818.pdf
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    References listed on IDEAS

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    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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    Cited by:

    1. Diankai Wang & Inna Gryshova & Mykola Kyzym & Tetiana Salashenko & Viktoriia Khaustova & Maryna Shcherbata, 2022. "Electricity Price Instability over Time: Time Series Analysis and Forecasting," Sustainability, MDPI, vol. 14(15), pages 1-24, July.

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    Keywords

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    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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