Skip to Main Content
Frequently Asked Questions
Submit an ETD
Global Search Box
Need Help?
Keyword Search
Participating Institutions
Advanced Search
School Logo
Files
File List
Masters Thesis.pdf (596.12 KB)
ETD Abstract Container
Abstract Header
Modeling Path Dependent Derivatives Using CUDA Parallel Platform
Author Info
Sterle, Lance
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=osu149263565284954
Abstract Details
Year and Degree
2017, Master of Mathematical Sciences, Ohio State University, Mathematical Sciences.
Abstract
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.
Committee
Chunsheng Ban (Advisor)
Edward Overman (Committee Member)
Pages
37 p.
Subject Headings
Finance
;
Mathematics
Keywords
Parallel Computing
;
Derivatives Pricing
;
Stochastic Calculus
;
Monte Carlo Simulation
Recommended Citations
Refworks
EndNote
RIS
Mendeley
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)
Abstract Footer
Document number:
osu149263565284954
Download Count:
962
Copyright Info
© 2017, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.