Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/62376
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dc.contributor.authorMisiran, M.-
dc.contributor.authorLu, Z.-
dc.contributor.authorTeo, K.-
dc.date.issued2010-
dc.identifier.citationProceedings of the International Conference on Optimization and Control (ICOCO2010), held in Guiyang China, July 18-23, 2010 / H. Xu, X. Yang and W. Wei (eds.): pp.573-588-
dc.identifier.isbn9780646534923-
dc.identifier.urihttp://hdl.handle.net/2440/62376-
dc.description.abstractGeometric fractional Brownian motion (GFBM) is an extended model of the traditional geometric Brownian motion that is widely used for Black-Scholes option pricing. By considering GFBM, we are now able to capture the memory dependency. This method will enable us to derive the estimators of the drift, _, volatility, _2, and also the index of self similarity, H, simultaneously. This will enable us to use the fractional Black-Scholes model with all the needed parameters. Simulation outcomes illustrate that our methodology is efficient and reliable. Empirical application to stock exchange index with option pricing under GFBM is also made.-
dc.description.statementofresponsibilityMasnita Misiran, Zudi Lu and Kok Lay Teo-
dc.language.isoen-
dc.publisherCurtin UT-
dc.rightsCopyright status unknown-
dc.source.urihttp://sci.gzu.edu.cn/a/icoco.html-
dc.subjectmaximum likelihood estimation-
dc.subjectgeometric fractional Brownian motion-
dc.subjectlong memory-
dc.subjectoption pricing-
dc.titleFractional Black-Scholes models: complete MLE with application to fractional option pricing-
dc.typeConference paper-
dc.contributor.conferenceInternational Conference on Optimization and Control (2010 : Guiyang, China)-
dc.publisher.placeCD-
pubs.publication-statusPublished-
Appears in Collections:Aurora harvest
Mathematical Sciences publications

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