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

An 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.

Identiferoai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/4989
Date07 December 2011
CreatorsSharma, Bhanu
ContributorsThulasiram,Ruppa (Computer Science) Thulasiraman,Parimala (Computer science), Irani,Pourang (Computer Science) Appadoo,Raj (Asper School of Business)
Source SetsUniversity of Manitoba Canada
Detected LanguageEnglish

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