Pricing swing options in electricity markets with two stochastic factors using a partial differential equation approach
| UDC.coleccion | Investigación | es_ES |
| UDC.departamento | Matemáticas | es_ES |
| UDC.endPage | 107 | es_ES |
| UDC.grupoInv | Modelos e Métodos Numéricos en Enxeñaría e Ciencias Aplicadas (M2NICA) | es_ES |
| UDC.issue | 3 | es_ES |
| UDC.journalTitle | Journal of Computational Finance | es_ES |
| UDC.startPage | 81 | es_ES |
| UDC.volume | 20 | es_ES |
| dc.contributor.author | Calvo-Garrido, María-del-Carmen | |
| dc.contributor.author | Vázquez, Carlos | |
| dc.contributor.author | Ehrhardt, Matthias | |
| dc.date.accessioned | 2024-01-30T15:06:43Z | |
| dc.date.available | 2024-01-30T15:06:43Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | [Abstract] In this paper, we consider the numerical valuation of swing options in electricity markets based on a two-factor model. These kinds of contracts are modeled as pathdependent options with multiple exercise rights. From a mathematical point of view, the valuation of these products is posed as a sequence of free boundary problems, where two exercise rights are separated by a time period. In order to solve the pricing problem, we propose appropriate numerical methods based on a Crank–Nicolson semi-Lagrangian method combined with biquadratic Lagrange finite elements for the discretization of the partial differential equation. In addition, we use an augmented Lagrangian active set method to cope with the early exercise feature when it appears. Moreover, we derive appropriate artificial boundary conditions to treat the unbounded domain numerically. Finally, we present some numerical results to illustrate the proper behavior of the numerical schemes. | es_ES |
| dc.description.sponsorship | This work has been partly funded by MINECO of Spain (Project MTM2013-47800-C2-1-P), by MICINN of Spain (FPI grant BES- 2011-044746) and by the German Federal Ministry of Education and Research (Project 57049700 2014 DAAD). | es_ES |
| dc.description.sponsorship | Germany. Federal Ministry of Education and Research; 57049700 2014 DAAD | es_ES |
| dc.identifier.citation | Calvo-Garrido, M.C., Ehrhardt, M. & Vázquez, C. (2017) Pricing swing options in electricity markets with two stochastic factors using a partial differential equations approach, Journal of Computational Finance, 20 (2017), 3, 81-107. https://doi.org/10.21314/JCF.2016.317 | es_ES |
| dc.identifier.doi | 10.21314/JCF.2016.317 | |
| dc.identifier.uri | http://hdl.handle.net/2183/35239 | |
| dc.language.iso | eng | es_ES |
| dc.relation.projectID | Info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2013-47800-C2-1-P/ES/MODELADO MATEMATICO, ANALISIS Y SIMULACION NUMERICA DE PROBLEMAS EN FINANZAS Y SEGUROS, PROCESOS INDUSTRIALES, BIOTECNOLOGIA Y MEDIOAMBIENTE | es_ES |
| dc.relation.projectID | Info:eu-repo/grantAgreement/MCINN/ Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2013-47800-C2-1-P/ES/ MODELADO MATEMATICO, ANALISIS Y SIMULACION NUMERICA DE PROBLEMAS EN FINANZAS Y SEGUROS, PROCESOS INDUSTRIALES, BIOTECNOLOGIA Y MEDIOAMBIENTE | es_ES |
| dc.relation.uri | https://doi.org/10.21314/JCF.2016.317 | es_ES |
| dc.rights | Copyright © 2016 Incisive Risk Information (IP) Limited | es_ES |
| dc.rights.accessRights | open access | es_ES |
| dc.subject | Swing options | es_ES |
| dc.subject | Electricity price | es_ES |
| dc.subject | Augmented Lagrangian Active Set (ALAS) Formulation | es_ES |
| dc.subject | Semi-Lagrangian method, Biquadratic Lagrange finite elements | es_ES |
| dc.subject | Artificial boundary conditions | es_ES |
| dc.title | Pricing swing options in electricity markets with two stochastic factors using a partial differential equation approach | es_ES |
| dc.type | journal article | es_ES |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 035f18a3-78da-4970-a577-b39e5748a9a4 | |
| relation.isAuthorOfPublication | dbc2be8e-6741-46b3-a22e-b648eae643d4 | |
| relation.isAuthorOfPublication.latestForDiscovery | 035f18a3-78da-4970-a577-b39e5748a9a4 |
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