{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:11:47Z","timestamp":1760710307414,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T00:00:00Z","timestamp":1594339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014440","name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["ENE-2016-78509-C3-3-P"],"award-info":[{"award-number":["ENE-2016-78509-C3-3-P"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>This article presents an original predictive strategy, based on a new mid-term forecasting model, to be used for trading physical electricity futures. The forecasting model is used to predict the average spot price, which is used to estimate the Risk Premium corresponding to electricity futures trade operations with a physical delivery. A feed-forward neural network trained with the extreme learning machine algorithm is used as the initial implementation of the forecasting model. The predictive strategy and the forecasting model only need information available from electricity derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and the performances of the forecasting model and of the predictive strategy were tested with data corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark trading strategies are also presented and evaluated using the Risk Premium in the testing period, for comparative purposes. The results prove the advantages of the predictive strategy, even using the simpler forecasting model, which showed improvements over the conventional benchmark trading strategy, evincing an interesting hedging potential for electricity futures trading.<\/jats:p>","DOI":"10.3390\/en13143555","type":"journal-article","created":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T09:25:28Z","timestamp":1594373128000},"page":"3555","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Predictive Trading Strategy for Physical Electricity Futures"],"prefix":"10.3390","volume":"13","author":[{"given":"Claudio","family":"Monteiro","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5633-4849","authenticated-orcid":false,"given":"L. Alfredo","family":"Fernandez-Jimenez","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of La Rioja, 26004 Logro\u00f1o, Spain"}]},{"given":"Ignacio J.","family":"Ramirez-Rosado","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of Zaragoza, 50018 Zaragoza, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,10]]},"reference":[{"key":"ref_1","unstructured":"Edwards, D.W. (2009). Energy Trading and Investing: Trading, Risk Management and Structuring Deals in the Energy Market, McGraw-Hill."},{"key":"ref_2","unstructured":"Economic Consulting Associates Limited (2020, April 28). European Electricity: Forward Markets and Hedging Products\u2014State of Play and Elements for Monitoring 2015. Available online: www.acer.europa.eu."},{"key":"ref_3","unstructured":"Hull, J. (2015). Options, Futures, and Other Derivatives, Prentice Hall. [9th ed.]."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1111\/1540-6261.00463","article-title":"Equilibrium pricing and optimal hedging in electricity forward markets","volume":"57","author":"Bessembinder","year":"2002","journal-title":"J. Financ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1002\/fut.20246","article-title":"The pricing of electricity futures: Evidence from the European energy exchange","volume":"27","author":"Wilkens","year":"2007","journal-title":"J. Futures Mark."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1016\/j.enpol.2009.10.023","article-title":"Expectations and forward risk premium in the Spanish deregulated power market","volume":"38","author":"Furio","year":"2010","journal-title":"Energy Policy"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dorsman, A., Westerman, W., Karan, M.B., and Arslan, \u00d6. (2011). The Electricity Market, Day-Ahead Market and Futures Market. Financial Aspects in Energy, Springer.","DOI":"10.1007\/978-3-642-19709-3"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105","DOI":"10.5547\/01956574.40.1.rste","article-title":"Short- to mid-term day-ahead electricity price forecasting using futures","volume":"40","author":"Steinert","year":"2019","journal-title":"Energy J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1016\/j.eneco.2012.04.008","article-title":"Electricity Futures Prices: Indirect Storability, Expectations, and Risk Premiums","volume":"34","author":"Huisman","year":"2012","journal-title":"Energy Econ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1086\/296385","article-title":"Commodity futures prices: Some evidence on forecast power, premiums, and the theory of storage","volume":"60","author":"Fama","year":"1987","journal-title":"J. Bus."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Benth, F., Kholodnyi, V., and Laurence, P. (2014). An analysis of the main determinants of electricity forward prices and forward risk Premia. Quantitative Energy Finance, Springer.","DOI":"10.1007\/978-1-4614-7248-3"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.1111\/j.1540-6261.2004.00682.x","article-title":"Electricity forward prices: A high-frequency empirical analysis","volume":"59","author":"Longstaff","year":"2004","journal-title":"J. Financ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2006","DOI":"10.1016\/j.jbankfin.2007.12.022","article-title":"Pricing forward contracts in power markets by the certainty equivalence principle: Explaining the sign of the market risk premium","volume":"32","author":"Benth","year":"2008","journal-title":"J. Bank Financ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.eneco.2014.03.007","article-title":"Revisiting the relationship between spot and futures prices in the Nord Pool electricity market","volume":"44","author":"Weron","year":"2014","journal-title":"Energy Econ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.eneco.2014.12.022","article-title":"The overnight risk premium in electricity forward contracts","volume":"49","author":"Fleten","year":"2015","journal-title":"Energy Econ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.jempfin.2013.06.002","article-title":"The forward premium in electricity futures","volume":"23","author":"Bunn","year":"2013","journal-title":"J. Empir. Financ."},{"key":"ref_17","unstructured":"(2020, April 29). The Iberian Energy Derivatives Exchange, OMIP. Available online: http:\/\/www.omip.pt."},{"key":"ref_18","first-page":"135","article-title":"Market efficiency and price discovery relationships between spot, futures and forward prices: The case of the Iberian Electricity Market (MIBEL)","volume":"45","author":"Ballester","year":"2016","journal-title":"Span. J. Financ. Accoun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"61","DOI":"10.21314\/JEM.2018.176","article-title":"The iberian electricity market: Analysis of the risk premium in an illiquid market","volume":"11","author":"Ferreira","year":"2018","journal-title":"J. Energy Markets"},{"key":"ref_20","first-page":"59","article-title":"Iberian electricity market spot and futures prices: Comovement and lead-lag relationship analysis","volume":"19","author":"Dias","year":"2019","journal-title":"J. Sustain. Energy Plan. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1016\/j.ijforecast.2016.02.001","article-title":"Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond","volume":"32","author":"Hong","year":"2016","journal-title":"Int. J. Forecast."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ijepes.2013.04.006","article-title":"Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach","volume":"53","author":"Yan","year":"2013","journal-title":"Int. J. Electr. Power"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1016\/j.eneco.2011.07.005","article-title":"Forecasting electricity prices and their volatilities using unobserved components","volume":"33","year":"2011","journal-title":"Energy Econ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.ijepes.2014.01.023","article-title":"Mid-term electricity market clearing price forecasting: A multiple SVM approach","volume":"58","author":"Yan","year":"2014","journal-title":"Int. J. Electr. Power"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ijepes.2014.03.007","article-title":"A time series spot price forecast model for the Nord Pool market","volume":"61","author":"Kristiansen","year":"2014","journal-title":"Int. J. Electr. Power"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Torbaghan, S.S., Motamedi, A., Zareipour, H., and Tuan, L.A. (2012, January 9\u201311). Medium-term electricity price forecasting. Proceedings of the 2012 North American Power Symposium (NAPS), Champaign, IL, USA.","DOI":"10.1109\/NAPS.2012.6336424"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cheng, C., Luo, B., Miao, S., and Wu, X. (2016). Mid-term electricity market clearing price forecasting with sparse data: A case in newly-reformed Yunnan electricity market. Energies, 9.","DOI":"10.3390\/en9100804"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.eneco.2005.01.002","article-title":"Stochastic factor model for electricity spot price\u2014the case of the Nordic market","volume":"27","year":"2005","journal-title":"Energy Econ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/TPWRS.2015.2416433","article-title":"Short-and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals","volume":"31","author":"Maciejowska","year":"2016","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_30","first-page":"75","article-title":"Evolutionary extreme learning machine for energy price forecasting","volume":"20","author":"Chakravarty","year":"2016","journal-title":"Int. J. Knowl.-Based Intell. Eng. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"117910","DOI":"10.1016\/j.jclepro.2019.117910","article-title":"One month-ahead electricity price forecasting in the context of production planning","volume":"238","author":"Windler","year":"2019","journal-title":"J. Clean Prod."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.rser.2018.05.038","article-title":"Probabilistic mid- and long-term electricity price forecasting","volume":"94","author":"Ziel","year":"2018","journal-title":"Renew. Sustain. Energ. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.eneco.2019.05.006","article-title":"Machine learning in energy economics and finance: A review","volume":"81","author":"Ghoddusi","year":"2019","journal-title":"Energy Econ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1016\/j.enpol.2008.10.033","article-title":"Trading strategies modeling in Colombian power market using artificial intelligence techniques","volume":"37","author":"Moreno","year":"2009","journal-title":"Energy Policy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.neucom.2015.03.102","article-title":"Support Vector Machines for decision support in electricity markets\u2019 strategic bidding","volume":"172","author":"Pinto","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.energy.2016.05.127","article-title":"MASCEM: Optimizing the performance of a multi-agent system","volume":"111","author":"Santos","year":"2016","journal-title":"Energy"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lopes, F. (2018). MATREM: An Agent-based Simulation Tool for Electricity Markets. Electricity Markets with Increasing Levels of Renewable Generation: Structure, Operation, Agent-Based Simulation and Emerging Designs, Springer.","DOI":"10.1007\/978-3-319-74263-2"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1109\/TPWRS.2019.2938423","article-title":"Towards Definition of the Risk Premium Function","volume":"35","author":"Benth","year":"2020","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Browell, J. (2018). Risk Constrained Trading Strategies for Stochastic Generation with a Single-Price Balancing Market. Energies, 11.","DOI":"10.3390\/en11061345"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bunn, D.W., Gianfreda, A., and Kermer, S. (2018). A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market. Energies, 11.","DOI":"10.3390\/en11102658"},{"key":"ref_41","unstructured":"(2020, June 12). MIBEL Board of Regulators. Estudio Sobre Comparativa de los Precios MIBEL (Contado y Plazo) con otros Mercados Europeos y su Relaci\u00f3n con el Mercado \u00fanico. Available online: https:\/\/www.mibel.com\/wp-content\/uploads\/2020\/03\/20190705SE_ESb.pdf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","article-title":"Extreme learning machines: A survey","volume":"2","author":"Huang","year":"2011","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.epsr.2015.01.002","article-title":"Short-term load forecasting by wavelet transform and evolutionary extreme learning machine","volume":"122","author":"Li","year":"2015","journal-title":"Electr. Power Syst. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neucom.2018.05.068","article-title":"Mixed kernel based extreme learning machine for electric load forecasting","volume":"312","author":"Chen","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6961","DOI":"10.1109\/TSG.2018.2807845","article-title":"Probabilistic load forecasting using an improved wavelet neural network trained by generalized extreme learning machine","volume":"9","author":"Rafiei","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.jclepro.2017.08.081","article-title":"Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems","volume":"167","author":"Hossain","year":"2017","journal-title":"J. Clean Prod."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1109\/TPWRS.2013.2287871","article-title":"Probabilistic forecasting of wind power generation using extreme learning machine","volume":"29","author":"Wan","year":"2014","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_49","first-page":"156","article-title":"A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm","volume":"23","author":"Behera","year":"2020","journal-title":"Eng. Sci. Technol. Inter. J."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.renene.2019.04.157","article-title":"A novel hybrid system based on multi-objective optimization for wind speed forecasting","volume":"146","author":"Wu","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1109\/TPWRS.2012.2190627","article-title":"Electricity price forecasting with extreme learning machine and bootstrapping","volume":"27","author":"Chen","year":"2012","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.ijepes.2013.08.023","article-title":"A hybrid wavelet-ELM based short term price forecasting for electricity markets","volume":"55","author":"Shrivastava","year":"2014","journal-title":"Int. J. Electr. Power"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, G., Wei, Y., and Qiao, S. (2018). Generalized Inverses: Theory and Computations, Springer.","DOI":"10.1007\/978-981-13-0146-9"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s10462-013-9405-z","article-title":"Extreme learning machine: Algorithm, theory and applications","volume":"44","author":"Ding","year":"2015","journal-title":"Artif. Intell. Rev."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1016\/j.eneco.2009.11.009","article-title":"The relationship between spot and futures prices in the Nord Pool electricity market","volume":"32","author":"Botterud","year":"2010","journal-title":"Energy Econ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1080\/07350015.1995.10524599","article-title":"Comparing predictive accuracy","volume":"13","author":"Diebold","year":"1995","journal-title":"J. Bus. Econ. Stat."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0169-2070(96)00719-4","article-title":"Testing the equality of prediction mean squared errors","volume":"13","author":"Harvey","year":"1997","journal-title":"Int. J. Forecast."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.eneco.2014.07.014","article-title":"An empirical comparison of alternative schemes for combining electricity spot price forecasts","volume":"46","author":"Nowotarski","year":"2014","journal-title":"Energy Econ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.eneco.2018.10.005","article-title":"The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts","volume":"76","author":"Kath","year":"2018","journal-title":"Energy Econ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.apenergy.2018.02.069","article-title":"Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms","volume":"221","author":"Lago","year":"2018","journal-title":"Appl. Energy"}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/13\/14\/3555\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:49:45Z","timestamp":1760176185000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/13\/14\/3555"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,10]]},"references-count":60,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["en13143555"],"URL":"https:\/\/doi.org\/10.3390\/en13143555","relation":{},"ISSN":["1996-1073"],"issn-type":[{"type":"electronic","value":"1996-1073"}],"subject":[],"published":{"date-parts":[[2020,7,10]]}}}