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2020 | OriginalPaper | Chapter

A CUDA Approach for Scenario Reduction in Hedging Models

Authors : Donald Davendra, Chin-mei Chueh, Emmanuel Hamel

Published in: AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application

Publisher: Springer International Publishing

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Abstract

A CUDA kernel is proposed in this paper for acceleration of the computation of a dynamic hedging model. This is a very useful tool in segregated fund modelling. Current approaches delve on scenario reduction techniques in order to extract meaningful information from a large data set. Parallel programming allows these models to be effectively evaluated within a critical time frame. The GPU execution times shows significant improvement over CPU approaches.

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Metadata
Title
A CUDA Approach for Scenario Reduction in Hedging Models
Authors
Donald Davendra
Chin-mei Chueh
Emmanuel Hamel
Copyright Year
2020
Book
AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application

Print ISBN: 978-3-030-14906-2

Electronic ISBN: 978-3-030-14907-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-14907-9_14