A Normalized Particle Swarm Optimization Algorithm to Price Complex Chooser Option and Accelerating its Performance with GPU

dc.contributor.authorSharma, Bhanu
dc.contributor.examiningcommitteeIrani,Pourang (Computer Science) Appadoo,Raj (Asper School of Business)en_US
dc.contributor.supervisorThulasiram,Ruppa (Computer Science) Thulasiraman,Parimala (Computer science)en_US
dc.date.accessioned2011-12-07T16:20:49Z
dc.date.available2011-12-07T16:20:49Z
dc.date.issued2011-12-07
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractAn option is a financial instrument which derives its value from an underlying asset. There are a wide range of options traded today. Some are simple and plain, like the European options, while others are very difficult to evaluate. Both buyers and sellers continue to look for efficient algorithms and faster technology to price options for profit. In this thesis, I will first map the PSO parameters to the parameters in the option pricing problem. Then, I extend this to study pricing of complex chooser option. Further, I design a parallel algorithm that avails of the inherent concurrency in PSO while searching for a optimum solution. For implementation of my algorithm I used graphics processor unit (GPU). Analyzing the characteristics of PSO and option pricing, I propose a strategy to normalize some of the PSO parameters that helps in better understanding the sensitivity of various parameters on option pricing results.en_US
dc.description.noteFebruary 2012en_US
dc.identifier.urihttp://hdl.handle.net/1993/4989
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectPSOen_US
dc.subjectGPUen_US
dc.subjectComputational Financeen_US
dc.subjectOptionsen_US
dc.subjectComplex chooseren_US
dc.subjectPortfolioen_US
dc.titleA Normalized Particle Swarm Optimization Algorithm to Price Complex Chooser Option and Accelerating its Performance with GPUen_US
dc.typemaster thesisen_US
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