Parallel implementation of option pricing methods on multiple GPUs

The Heston stochastic volatility model is one of the most popular models for the evolution of stocks and futures prices, which includes a stochastic process for the volatility. In practice it is usually enhanced by adding a Poisson jump process, which improves the overall correspondence with the observed behaviour of prices in the marketplace. The pricing of financial options by means of Monte Carlo or quasi-Monte Carlo methods can greatly benefit from the use of GPU computing due to the inherent parallelism of the computations. In this work we describe efficient parallel implementations of several popular option pricing schemes by using CUDA-enabled graphic cards. Our quasi-Monte Carlo algorithms make use of modifications of the Sobol and Halton sequences. The numerical and timing results demonstrate the excellent efficiency of our approach on the target computational platforms.

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