IDEAS home Printed from https://ideas.repec.org/a/aic/saebjn/v67y2020isip1-17n191.html
   My bibliography  Save this article

Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures

Author

Listed:
  • Helder Sebastião
  • Pedro Godinho
  • Sjur Westgaard

Abstract

This study investigates the use of several trading strategies, based on Machine Learning methods, to profit on the risk premium of the Nordic electricity base-load week futures. The information set is only composed by financial data from January 02, 2006 to November 15, 2017. The results point out that the Support Vector Machine is the best method, but, most importantly, they highlight that all individual models are valuable, in the sense that their combination provides a robust trading procedure, generating an average profit of at least 26% per year, after considering trading costs and liquidity constraints. The results are robust to the different data partitions, and there is no evidence that the profitability of the trading strategies has decreased in recent years. We claim that this market allows for profitable speculation, namely by using combinations of non-linear signal extraction techniques. JEL Codes - G13; G14; Q40

Suggested Citation

  • Helder Sebastião & Pedro Godinho & Sjur Westgaard, 2020. "Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 67(si), pages 1-17, December.
    • Handle: RePEc:aic:saebjn:v:67:y:2020:i:si:p:1-17:n:191
    DOI: 10.47743/saeb-2020-0024
    as

    Download full text from publisher

    File URL: http://saeb.feaa.uaic.ro/index.php/saeb/article/view/1194
    Download Restriction: no
    File URL: https://libkey.io/10.47743/saeb-2020-0024?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Cartea, Álvaro & Villaplana, Pablo, 2008. "Spot price modeling and the valuation of electricity forward contracts: The role of demand and capacity," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2502-2519, December.
    2. 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.
    3. Huisman, Ronald & Kilic, Mehtap, 2012. "Electricity Futures Prices: Indirect Storability, Expectations, and Risk Premiums," Energy Economics, Elsevier, vol. 34(4), pages 892-898.
    4. Lubnau, Thorben & Todorova, Neda, 2015. "Trading on mean-reversion in energy futures markets," Energy Economics, Elsevier, vol. 51(C), pages 312-319.
    5. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei Wei & Asger Lunde, 2023. "Identifying Risk Factors and Their Premia: A Study on Electricity Prices," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1647-1679.
    2. repec:dui:wpaper:1504 is not listed on IDEAS
    3. Algieri, Bernardina & Leccadito, Arturo & Tunaru, Diana, 2021. "Risk premia in electricity derivatives markets," Energy Economics, Elsevier, vol. 100(C).
    4. Pape, Christian & Hagemann, Simon & Weber, Christoph, 2016. "Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market," Energy Economics, Elsevier, vol. 54(C), pages 376-387.
    5. Koolen, Derck & Huisman, Ronald & Ketter, Wolfgang, 2022. "Decision strategies in sequential power markets with renewable energy," Energy Policy, Elsevier, vol. 167(C).
    6. repec:dui:wpaper:1502 is not listed on IDEAS
    7. 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.
    8. Spodniak, Petr & Bertsch, Valentin, 2017. "Determinants of power spreads in electricity futures markets: A multinational analysis," Papers WP580, Economic and Social Research Institute (ESRI).
    9. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    10. Jannik Schütz Roungkvist & Peter Enevoldsen & George Xydis, 2020. "High-Resolution Electricity Spot Price Forecast for the Danish Power Market," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
    11. Deschatre, Thomas & Féron, Olivier & Gruet, Pierre, 2021. "A survey of electricity spot and futures price models for risk management applications," Energy Economics, Elsevier, vol. 102(C).
    12. Damir Filipovic & Martin Larsson & Tony Ware, 2017. "Polynomial processes for power prices," Papers 1710.10293, arXiv.org, revised Apr 2018.
    13. 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.
    14. Kallabis, Thomas & Pape, Christian & Weber, Christoph, 2016. "The plunge in German electricity futures prices – Analysis using a parsimonious fundamental model," Energy Policy, Elsevier, vol. 95(C), pages 280-290.
    15. Nikola Krečar & Andrej F. Gubina, 2020. "Risk mitigation in the electricity market driven by new renewable energy sources," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(1), January.
    16. 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.
    17. Werner, Dan, 2014. "Electricity Market Price Volatility: The Importance of Ramping Costs," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169619, Agricultural and Applied Economics Association.
    18. Lavička, Hynek & Kracík, Jiří, 2020. "Fluctuation analysis of electric power loads in Europe: Correlation multifractality vs. Distribution function multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    19. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    20. Andrey Zahariev & Mikhail Zveryаkov & Stoyan Prodanov & Galina Zaharieva & Petko Angelov & Silvia Zarkova & Mariana Petrova, 2020. "Debt management evaluation through Support Vector Machines: on the example of Italy and Greece," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(3), pages 2382-2393, March.
    21. Tai, Chung-Ching & Lin, Hung-Wen & Chie, Bin-Tzong & Tung, Chen-Yuan, 2019. "Predicting the failures of prediction markets: A procedure of decision making using classification models," International Journal of Forecasting, Elsevier, vol. 35(1), pages 297-312.
    22. Afanasyev, Dmitriy O. & Fedorova, Elena A. & Popov, Viktor U., 2015. "Fine structure of the price–demand relationship in the electricity market: Multi-scale correlation analysis," Energy Economics, Elsevier, vol. 51(C), pages 215-226.

    More about this item

    Keywords

    Nord Pool; electricity futures; risk premium; machine learning; trading;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aic:saebjn:v:67:y:2020:i:si:p:1-17:n:191. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sireteanu Napoleon-Alexandru (email available below). General contact details of provider: https://edirc.repec.org/data/feaicro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.