Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/27109
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dc.contributor.authorThakur, P-
dc.contributor.authorHesamzadeh, M-
dc.contributor.authorDate, P-
dc.contributor.authorBunn, D-
dc.date.accessioned2023-09-01T15:24:28Z-
dc.date.available2023-09-01T15:24:28Z-
dc.date.issued2023-08-25-
dc.identifierORCID iDs: Mohammad Reza Hesamzadeh https://orcid.org/0000-0002-9998-9773; Paresh Date https://orcid.org/0000-0001-7097-9961.-
dc.identifier106960-
dc.identifier.citationThakur, P. et al. (2023) 'Pricing and hedging wind power prediction risk with binary option contracts', Energy Economics, 0 (in press, corrected proof), pp. 1 - 24. doi: 10.1016/j.eneco.2023.106960.en_US
dc.identifier.issn0140-9883-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/27109-
dc.descriptionAppendix. Descriptive statistics for the payoff and classification accuracy of various classifiers are available online at https://www.sciencedirect.com/science/article/pii/S0140988323004589#appendix .en_US
dc.description.abstractCopyright © 2023 The Author(s). In markets with a high proportion of wind generation, high wind outputs tend to induce low market prices and, alternatively, high prices often occur under low wind output conditions. Wind producer revenues are affected adversely in both situations. Whilst it is not possible to directly hedge revenues, it is possible to hedge wind speed with weather insurance and market prices with forward derivatives. Thus combined hedges are offered to the wind producers through bilateral arrangements and as a consequence, the risk managers of wind assets need to be able to forecast fair prices for them. We formulate these hedges as binary option contracts on the combined uncertainties of wind speed and market price and provide a new analysis, based upon machine learning classification, to forecast fair prices for such hedges. The proposed forecasting model achieves a classification accuracy of 88 percent and could therefore aid the wind producers in their negotiations with the hedge providers. Furthermore, in a realistic example, we find that the predicted costs of such hedges are quite affordable and should therefore become more widely adopted by the insurers and wind generators.en_US
dc.format.extent1 - 24-
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.rightsCopyright © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/).-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectwind poweren_US
dc.subjectforecastingen_US
dc.subjecthedgingen_US
dc.subjectquanto optionsen_US
dc.subjectdeep learningen_US
dc.subjectmulti-class classificationen_US
dc.subjectrisk managementen_US
dc.titlePricing and hedging wind power prediction risk with binary option contractsen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1016/j.eneco.2023.106960-
dc.relation.isPartOfEnergy Economics-
pubs.publication-statusPublished online-
pubs.volume0-
dc.identifier.eissn1873-6181-
dc.rights.holderThe Author(s)-
Appears in Collections:Dept of Mathematics Research Papers

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