Accelerating the Least-Squares Monte Carlo Methodwith Parallel Computing
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 102 === This thesis accelerates the popular least-squares Monte Carlo method (LSM) in finance with parallel computing. Several processes are created to solve LSM. Each process solves a smaller version of LSM independently before averaging the values calculated by all t...
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| Format: | Others |
| Language: | en_US |
| Published: |
2014
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| Online Access: | http://ndltd.ncl.edu.tw/handle/56655360426490392952 |
| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 102 === This thesis accelerates the popular least-squares Monte Carlo method (LSM) in finance with parallel computing. Several processes are created to solve LSM. Each process solves a smaller version of LSM independently before averaging the values calculated by all the processes. This methodology turns LSM into an embarrassingly parallel problem. The program is implemented using Parallel Virtual Machine (PVM) and ALGLIB. This thesis focuses on the pricing of American put options. Our proposed method gives accurate option prices with excellent speedups and achieves a speedup of 55 using 64 processes with 8 machines. The same methodology is expected to yield excellent speedups for LSM when applied to more complex financial derivatives.
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