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A note on using the Hodrick-Prescott filter in electricity markets

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  • Rafal Weron
  • Michal Zator

Abstract

Recently, Nowotarski et al. (2013) have found that wavelet-based models for the long-term seasonal component (LTSC) are not only better in extracting the LTSC from a series of spot electricity prices but also significantly more accurate in terms of forecasting these prices up to a year ahead than the commonly used monthly dummies and sine-based models. However, a clear disadvantage of the wavelet-based approach is the increased complexity of the technique as compared to the other two classes of LTSC models, which may render it too complicated for practitioners. To facilitate this problem, we propose here a much simpler, yet equally powerful method for identifying the LTSC in electricity spot price series. It makes use of the Hodrick-Prescott (HP) filter, a widely-recognized tool in macroeconomics.

Suggested Citation

  • Rafal Weron & Michal Zator, 2014. "A note on using the Hodrick-Prescott filter in electricity markets," HSC Research Reports HSC/14/04, Hugo Steinhaus Center, Wroclaw University of Technology.
    • Handle: RePEc:wuu:wpaper:hsc1404
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    References listed on IDEAS

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    Cited by:

    1. Maryniak, Paweł & Trück, Stefan & Weron, Rafał, 2019. "Carbon pricing and electricity markets — The case of the Australian Clean Energy Bill," Energy Economics, Elsevier, vol. 79(C), pages 45-58.
    2. Afanasyev, Dmitriy & Fedorova, Elena, 2015. "The long-term trends on Russian electricity market: comparison of empirical mode and wavelet decompositions," MPRA Paper 62391, University Library of Munich, Germany.
    3. Д.О. Афанасьев1 & * & Е.А. Федорова2 & **, 2019. "Краткосрочное Прогнозирование Цены Электроэнергии На Российском Рынке С Использованием Класса Моделей Scarx," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 55(1), pages 68-84, январь.
    4. Wei Wei & Asger Lunde, 2020. "Identifying Risk Factors and Their Premia: A Study on Electricity Prices," Monash Econometrics and Business Statistics Working Papers 10/20, Monash University, Department of Econometrics and Business Statistics.
    5. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, vol. 79(C), pages 171-182.
    6. Nowotarski, Jakub & Weron, Rafał, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 57(C), pages 228-235.
    7. Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2017. "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models," HSC Research Reports HSC/17/03, Hugo Steinhaus Center, Wroclaw University of Technology.
    8. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 466-479.
    9. Pawel Maryniak & Stefan Trueck & Rafal Weron, 2016. "Carbon pricing, forward risk premiums and pass-through rates in Australian electricity futures markets," HSC Research Reports HSC/16/10, Hugo Steinhaus Center, Wroclaw University of Technology.
    10. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    11. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    12. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    13. Alvarez-Ramirez, J. & Rodriguez, E. & Ibarra-Valdez, C., 2020. "Medium-term cycles in the dynamics of the Dow Jones Index for the period 1985–2019," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 546(C).
    14. Caldana, Ruggero & Fusai, Gianluca & Roncoroni, Andrea, 2017. "Electricity forward curves with thin granularity: Theory and empirical evidence in the hourly EPEXspot market," European Journal of Operational Research, Elsevier, vol. 261(2), pages 715-734.
    15. Ilaria Lucrezia Amerise & Agostino Tarsitano, 2020. "An L1 smoother for outlier cleaning of time series," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(1), pages 1-3.
    16. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2016. "The long-term trends on the electricity markets: Comparison of empirical mode and wavelet decompositions," Energy Economics, Elsevier, vol. 56(C), pages 432-442.

    More about this item

    Keywords

    Hodrick-Prescott filter; Electricity spot price; Long-term seasonal component; Robust modeling;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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