Multistep schemes for solving backward stochastic differential equations on GPU
Abstract The Backward Stochastic Differential Equation (BSDE) is an important tool for pricing and hedging. Highly accurate pricing for low computation time becomes interesting for minimizing monetary loss. Therefore, we explore the opportunity of parallelizing high-order multistep schemes in option...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-01-01
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| Series: | Journal of Mathematics in Industry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13362-021-00118-3 |
| Summary: | Abstract The Backward Stochastic Differential Equation (BSDE) is an important tool for pricing and hedging. Highly accurate pricing for low computation time becomes interesting for minimizing monetary loss. Therefore, we explore the opportunity of parallelizing high-order multistep schemes in option pricing. In the multistep scheme the computations at each space grid point are independent and this fact motivates us to select massively parallel GPU computing using CUDA. In our investigations we identify performance bottlenecks and apply appropriate optimization techniques to reduce the computation time in a uniform space domain. Runtime experiments manifest optimistic speedups for the parallel implementation on a single GPU, NVIDIA GeForce 1070 Ti. |
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| ISSN: | 2190-5983 |