A heterogeneous computing approach to maximum likelihood parameter estimation for the Heston model of stochastic volatility

Abstract

Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston model in which the price and volatility of an asset evolve as a pair of coupled stochastic differential equations. The computation of asset prices and volatilities involves the simulation of many sample trajectories with conditioning. The problem is treated using the method of particle filtering. While the simulation of a shower of particles is computationally expensive, each particle behaves independently making such simulations ideal for massively parallel heterogeneous computing platforms. We present a portable OpenCL implementation of the Heston model and discuss its performance and efficiency characteristics on a range of architectures including Intel CPUs, Nvidia GPUs, and Intel Many-Integrated-Core accelerators. References

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