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)


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.


CUDA Scenario reduction Hedging model 


  1. 1.
    Castillo, D., Ferreiro, A., García-Rodríguez, J., Vázquez, C.: Numerical methods to solve PDE models for pricing business companies in different regimes and implementation in GPUs. Appl. Math. Comput. 219(24), 11,233–11,257 (2013)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Chueh, Y.: Efficient stochastic modeling for large and consolidated insurance business: interest rate sampling algorithms. N. Am. Actuarial J. 6(3), 88–103 (2002)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chueh, Y.: Insurance modeling and stochastic cash flow scenario testing: effective sampling algorithms to reduce number of run and SALMS (Stochastic Asset Liability Modeling Sampling). Contingencies, pp. 1–18 (2003)Google Scholar
  4. 4.
    Chueh, Y.: Efficient stochastic modeling: Scenario sampling enhanced by parametric model outcome fitting. Contingencies, pp. 39–43 (2005)Google Scholar
  5. 5.
    Chueh, Y., Johnson, P.J.: CSTEP: a HPC platform for scenario reduction research on efficient stochastic modeling - representative scenario approach. Actuarial Res. Clearing House 1, 1–12 (2012)Google Scholar
  6. 6.
    Cook, S.: CUDA Programming: A Developer’s Guide to Parallel Computing with GPUs, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2013)Google Scholar
  7. 7.
    Davendra, D.D., Zelinka, I.: GPU Based Enhanced Differential Evolution Algorithm: A Comparison between CUDA and OpenCL, pp. 845–867. Springer, Heidelberg (2013)Google Scholar
  8. 8.
    Dixon, M.F., Bradley, T., Chong, J., Keutzer, K.: Monte Carlo-based financial market value-at-risk estimation on GPUs. In: Hwu, W.-H. (ed.) GPU Computing Gems Jade Edition, Applications of GPU Computing Series, pp. 337–353. Morgan Kaufmann, Boston (2012)CrossRefGoogle Scholar
  9. 9.
    Oliveira, F., Davendra, D., Guimarães, F.: Multi-objective differential evolution on the GPU with C-CUDA, vol. 188, pp. 123–132 (2013)Google Scholar

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

Personalised recommendations