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dc.contributor.authorFernández, J. L.
dc.contributor.authorFerreiro Ferreiro, Ana María
dc.contributor.authorGarcía Rodríguez, José Antonio
dc.contributor.authorLeitao, Álvaro
dc.contributor.authorLópez-Salas, José Germán
dc.contributor.authorVázquez, Carlos
dc.date.accessioned2024-07-17T10:15:53Z
dc.date.available2024-07-17T10:15:53Z
dc.date.issued2013-08
dc.identifier.citationJ. L. Fernández, A. M. Ferreiro, J. A. García-Rodríguez, A. Leitao, J. G. López-Salas, y C. Vázquez, «Static and dynamic SABR stochastic volatility models: Calibration and option pricing using GPUs», Mathematics and Computers in Simulation, vol. 94, pp. 55-75, ago. 2013, doi: 10.1016/j.matcom.2013.05.007.es_ES
dc.identifier.issn1872-7166
dc.identifier.issn0378-4754
dc.identifier.urihttp://hdl.handle.net/2183/38104
dc.description© 2013 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 Mathematics and Computers in Simulation (ISSN 1872-7166). The Version of Record is available online at 10.1016/j.matcom.2013.05.007.es_ES
dc.description.abstract[Abstract]: For the calibration of the parameters in static and dynamic SABR stochastic volatility models, we propose the application of the GPU technology to the Simulated Annealing global optimization algorithm and to the Monte Carlo simulation. This calibration has been performed for EURO STOXX 50 index and EUR/USD exchange rate with an asymptotic formula for volatility or Monte Carlo simulation. Moreover, in the dynamic model we propose an original more general expression for the functional parameters, specially well suited for the EUR/USD exchange rate case. Numerical results illustrate the expected behavior of both SABR models and the accuracy of the calibration. In terms of computational time, when the asymptotic formula for volatility is used the speedup with respect to CPU computation is around 200 with one GPU. Furthermore, GPU technology allows the use of Monte Carlo simulation for calibration purposes, the computational time with CPU being prohibitive.es_ES
dc.description.sponsorshipPartially financed by MCINN (Project MTM2010-21135-C02-01) and Ayuda CN2011/004 partially funded with FEDER funds. Also the authors would like to thank M. Menéndez, from Banesto Bank, for providing the market data.es_ES
dc.description.sponsorshipXunta de Galicia; CN2011/004es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/Plan Nacional de I+D+i 2008-2011/MTM2010-21135-C02-01/ES/MODELOS, ANALISIS MATEMATICO Y RESOLUCION NUMERICA DE ALGUNOS PROBLEMAS EN CIENCIA E INGENIERIA BASADOS EN EDPSes_ES
dc.relation.urihttps://doi.org/10.1016/j.matcom.2013.05.007es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectParallel Simulated Annealinges_ES
dc.subjectSABR volatility modeles_ES
dc.subjectCalibrationes_ES
dc.subjectGPUses_ES
dc.subjectCUDAes_ES
dc.titleStatic and dynamic SABR stochastic volatility models: Calibration and option pricing using GPUses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleMathematics and Computers in Simulationes_ES
UDC.volume94es_ES
UDC.startPage55es_ES
UDC.endPage75es_ES


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