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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Davendra, D., Chueh, Cm., Hamel, E. (2020). A CUDA Approach for Scenario Reduction in Hedging Models. In: Zelinka, I., Brandstetter, P., Trong Dao, T., Hoang Duy, V., Kim, S. (eds) AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2018. Lecture Notes in Electrical Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-030-14907-9_14
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DOI: https://doi.org/10.1007/978-3-030-14907-9_14
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