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dc.contributor.authorLópez Salas, José Germán
dc.contributor.authorVázquez, Carlos
dc.date.accessioned2024-07-18T09:12:22Z
dc.date.available2024-07-18T09:12:22Z
dc.date.issued2018-03-01
dc.identifier.citationJ. G. López-Salas y C. Vázquez, «PDE formulation of some SABR/LIBOR market models and its numerical solution with a sparse grid combination technique», Computers & Mathematics with Applications, vol. 75, n.o 5, pp. 1616-1634, mar. 2018, doi: 10.1016/j.camwa.2017.11.024.es_ES
dc.identifier.issn1873-7668
dc.identifier.issn0898-1221
dc.identifier.urihttp://hdl.handle.net/2183/38131
dc.description© 2018 Elsevier. This manuscript version is made available under the CCBY- NC-ND 4.0 license https://creativecommons.org/licenses/by-ncnd/ 4.0/. This version of the article has been accepted for publication in Computers & Mathematics with Applications. The Version of Record is available online at https://doi.org/10.1016/j.camwa.2017.11.024.es_ES
dc.description.abstract[Abstract]: SABR models have been used to incorporate stochastic volatility to LIBOR market models (LMM) in order to describe interest rate dynamics and price interest rate derivatives. From the numerical point of view, the pricing of derivatives with SABR/LIBOR market models (SABR/LMMs) is mainly carried out with Monte Carlo simulation. However, this approach could involve excessively long computational times. For first time in the literature, in the present paper we propose an alternative pricing based on partial differential equations (PDEs). Thus, we pose original PDE formulations associated to the SABR/LMMs proposed by Hagan and Lesniewsk (2008), Mercurio and Morini (2009) and Rebonato and White (2008). Moreover, as the PDEs associated to these SABR/LMMs are high dimensional in space, traditional full grid methods (like standard finite differences or finite elements) are not able to price derivatives over more than three or four underlying interest rates. In order to overcome this curse of dimensionality, a sparse grid combination technique is proposed. A comparison between Monte Carlo simulation results and the ones obtained with the sparse grid technique illustrates the performance of the method.es_ES
dc.description.sponsorshipPartially financed by Spanish Grant MTM2013-47800-C2-1-P and by Xunta de Galicia (Grant CN2014/044). First author has also been funded by a FPU Spanish Grant (AP2012-4975).es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo: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 MEDIOAMBIENTEes_ES
dc.relationinfo:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AP2012-4975/ES/es_ES
dc.relation.urihttps://doi.org/10.1016/j.camwa.2017.11.024es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectStochastic volatility modelses_ES
dc.subjectSABR/LIBOR market modelses_ES
dc.subjectHigh dimensional PDEses_ES
dc.subjectSparse gridses_ES
dc.subjectCombination techniquees_ES
dc.titlePDE formulation of some SABR/LIBOR market models and its numerical solution with a sparse grid combination techniquees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleComputers & Mathematics with Applicationses_ES
UDC.volume75es_ES
UDC.issue5es_ES
UDC.startPage1616es_ES
UDC.endPage1634es_ES


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