Pricing Financial Derivatives with High Performance Finite Difference Solvers on GPUs
Daniel Egloff
The calculation of the fair value and the sensitivity parameters of a financial derivative requires special numerical methods, which are often computationally very demanding. In this chapter we discuss the design and implementation of efficient GPU solvers for the partial differential equations (PDEs) of derivative pricing problems.
For derivatives on a single asset like a stock or an index we consider a massively parallel PDE solver which simultaneously prices a large collection of similar or related derivatives with finite difference schemes. We achieve a speedup of a factor of 25 on a single GPU and up to a factor of 40 on a dual ...
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