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Modeling Path Dependent Derivatives Using CUDA Parallel Platform

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2017, Master of Mathematical Sciences, Ohio State University, Mathematical Sciences.
The pricing of derivative securities with path dependence is governed by stochastic differential equations which rarely have a closed-form, analytic solution. These complex derivatives can be valued used simulation methods known as Monte Carlo methods, which converge slowly and thus require significant computational cost. This thesis demonstrates how the use of the GPU (Graphics Process Unit) can drastically lower the computational cost of these methods. The Longstaff;-Schwartz Least Squares Monte Carlo Method is implemented to price American options, and suggestions are made for improving the efficiency of the algorithm. A model for valuing Guaranteed Lifetime Withdrawal Benefit (GLWB) options using Monte Carlo methods is also proposed and implemented in CUDA's parallel environment. Finally, the sensitivity of the GLWB option to various factors and the ramifications for insurance companies who sell this guarantee is discussed.
Chunsheng Ban (Advisor)
Edward Overman (Committee Member)
37 p.

Recommended Citations

Citations

  • Sterle, L. (2017). Modeling Path Dependent Derivatives Using CUDA Parallel Platform [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu149263565284954

    APA Style (7th edition)

  • Sterle, Lance. Modeling Path Dependent Derivatives Using CUDA Parallel Platform. 2017. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu149263565284954.

    MLA Style (8th edition)

  • Sterle, Lance. "Modeling Path Dependent Derivatives Using CUDA Parallel Platform." Master's thesis, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu149263565284954

    Chicago Manual of Style (17th edition)