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Forecasting hourly electricity prices using ARMAX–GARCH models: An application to MISO hubs

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  • Hickey, Emily
  • Loomis, David G.
  • Mohammadi, Hassan

Abstract

The recent deregulation of the electricity industry and reliance on competitive wholesale markets has generated significant volatility in wholesale electricity prices. Given the importance of short-term price forecasts in this new environment, this paper estimates and evaluates the forecasting performance of four ARMAX–GARCH models for five MISO pricing hubs (Cinergy, First Energy, Illinois, Michigan, and Minnesota) using hourly data from June 1, 2006 to October 6, 2007. Our empirical results reveal three important patterns: (a) electricity price volatility is regional and the optimum volatility model depends in part on the hub location, the forecast horizon, and regulated versus unregulated status of the market; (b) the APARCH model performs well in hubs in deregulated states; and (c) volatility dynamics in regulated states are better captured by a simple GARCH model and thus are less complex.

Suggested Citation

  • Hickey, Emily & Loomis, David G. & Mohammadi, Hassan, 2012. "Forecasting hourly electricity prices using ARMAX–GARCH models: An application to MISO hubs," Energy Economics, Elsevier, vol. 34(1), pages 307-315.
  • Handle: RePEc:eee:eneeco:v:34:y:2012:i:1:p:307-315
    DOI: 10.1016/j.eneco.2011.11.011
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Jorge Barrientos Marín & Mónica Toro Martínez, 2016. "Sobre Los Fundamentales Del Precio De La Energía Eléctrica: Evidencia Empírica Para Colombia," Grupo Microeconomía Aplicada 74, Universidad de Antioquia, Departamento de Economía.
    2. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
    3. Erdogdu, Erkan, 2016. "Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis," Energy Economics, Elsevier, vol. 56(C), pages 398-409.
    4. repec:eee:energy:v:142:y:2018:i:c:p:1083-1103 is not listed on IDEAS
    5. S. Vijayalakshmi & G. P. Girish, 2015. "Artificial Neural Networks for Spot Electricity Price Forecasting: A Review," International Journal of Energy Economics and Policy, Econjournals, vol. 5(4), pages 1092-1097.
    6. Bello, Antonio & Reneses, Javier & Muñoz, Antonio & Delgadillo, Andrés, 2016. "Probabilistic forecasting of hourly electricity prices in the medium-term using spatial interpolation techniques," International Journal of Forecasting, Elsevier, vol. 32(3), pages 966-980.
    7. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
    8. G P Girish & Aviral Kumar Tiwari, 2016. "A comparison of different univariate forecasting models forSpot Electricity Price in India," Economics Bulletin, AccessEcon, vol. 36(2), pages 1039-1057.
    9. Ping Jiang & Feng Liu & Yiliao Song, 2016. "A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-27, August.
    10. repec:eco:journ4:2014-01-4 is not listed on IDEAS
    11. repec:eee:rensus:v:88:y:2018:i:c:p:297-325 is not listed on IDEAS
    12. Frömmel, Michael & Han, Xing & Kratochvil, Stepan, 2014. "Modeling the daily electricity price volatility with realized measures," Energy Economics, Elsevier, vol. 44(C), pages 492-502.
    13. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Energy Economics, Elsevier, vol. 51(C), pages 430-444.
    14. Qu, Hui & Chen, Wei & Niu, Mengyi & Li, Xindan, 2016. "Forecasting realized volatility in electricity markets using logistic smooth transition heterogeneous autoregressive models," Energy Economics, Elsevier, vol. 54(C), pages 68-76.

    More about this item

    Keywords

    Electricity; Pricing; MISO; GARCH; ARMAX;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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