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A CUDA Approach for Scenario Reduction in Hedging Models

  • Donald DavendraEmail author
  • Chin-mei Chueh
  • Emmanuel Hamel
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)

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.

Keywords

CUDA Scenario reduction Hedging model 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Donald Davendra
    • 1
    Email author
  • Chin-mei Chueh
    • 2
  • Emmanuel Hamel
    • 3
  1. 1.Department of Computer ScienceCentral Washington UniversityEllensburgUSA
  2. 2.Department of MathematicsCentral Washington UniversityEllensburgUSA
  3. 3.Autorité des marchés financiersQuébecCanada

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