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Comparison of Sobol’ sequences in financial applications

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  • Harase Shin

    (College of Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga, 525-8577, Japan)

Abstract

Sobol’ sequences are widely used for quasi-Monte Carlo methods that arise in financial applications. Sobol’ sequences have parameter values called direction numbers, which are freely chosen by the user, so there are several implementations of Sobol’ sequence generators. The aim of this paper is to provide a comparative study of (non-commercial) high-dimensional Sobol’ sequences by calculating financial models. Additionally, we implement the Niederreiter sequence (in base 2) with a slight modification, that is, we reorder the rows of the generating matrices, and analyze and compare it with the Sobol’ sequences.

Suggested Citation

  • Harase Shin, 2019. "Comparison of Sobol’ sequences in financial applications," Monte Carlo Methods and Applications, De Gruyter, vol. 25(1), pages 61-74, March.
    • Handle: RePEc:bpj:mcmeap:v:25:y:2019:i:1:p:61-74:n:3
    DOI: 10.1515/mcma-2019-2029
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    References listed on IDEAS

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    1. S. Ninomiya & S. Tezuka, 1996. "Toward real-time pricing of complex financial derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 3(1), pages 1-20.
    2. Xiaoqun Wang & Ken Seng Tan, 2013. "Pricing and Hedging with Discontinuous Functions: Quasi-Monte Carlo Methods and Dimension Reduction," Management Science, INFORMS, vol. 59(2), pages 376-389, July.
    3. Corwin Joy & Phelim P. Boyle & Ken Seng Tan, 1996. "Quasi-Monte Carlo Methods in Numerical Finance," Management Science, INFORMS, vol. 42(6), pages 926-938, June.
    4. Xiaoqun Wang, 2016. "Handling Discontinuities in Financial Engineering: Good Path Simulation and Smoothing," Operations Research, INFORMS, vol. 64(2), pages 297-314, April.
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