Accelerating the Least-Squares Monte Carlo Method with Parallel Computing
Date Issued
2014
Date
2014
Author(s)
Chen, Ching-Wen
Abstract
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.
Subjects
最小平方蒙地卡羅法
平行運算
選擇權評價
尷尬平行
平行虛擬機
Type
thesis
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ntu-103-R01922005-1.pdf
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