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Accelerating the least-square Monte Carlo method with parallel computing

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Abstract

This paper accelerates the critically important least-squares Monte Carlo method (LSM) in financial derivatives pricing with parallel computing. We parallelize LSM with space decomposition, turning it into an embarrassingly parallel algorithm. The program is implemented with Parallel Virtual Machine and ALGLIB. Our method gives accurate option prices with excellent speedup. Although this paper focuses on the pricing of options, the methodology is applicable to much more complex financial derivatives.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan

    Ching-Wen Chen & Kuan-Lin Huang

  2. Department of Computer Science and Information Engineering, Department of Finance, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan

    Yuh-Dauh Lyuu

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  1. Ching-Wen Chen
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  2. Kuan-Lin Huang
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  3. Yuh-Dauh Lyuu
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Correspondence to Ching-Wen Chen.

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Chen, CW., Huang, KL. & Lyuu, YD. Accelerating the least-square Monte Carlo method with parallel computing. J Supercomput 71, 3593–3608 (2015). https://doi.org/10.1007/s11227-015-1451-7

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  • DOI: https://doi.org/10.1007/s11227-015-1451-7

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