A Portable and Fast Stochastic Volatility Model Calibration Using Multi and Many-Core Processors

Abstract

Financial markets change precipitously and on-demand pricing and risk models must be constantly recalibrated to reduce risk. However, certain classes of models are computationally intensive to robustly calibrate to intraday pricesstochastic volatility models being an archetypal example due to the non-convexity of the objective function. In order to accelerate this procedure through parallel implementation,nancial application developers are faced with an ever growing plethora of low-level high-performance computing frameworks such as OpenMP, OpenCL, CUDA, or SIMD intrinsics, and forced to make a trade-off between performance versus the portability,exibility and modularity of the code required to facilitate rapid in-house model development and productionization.This paper describes the acceleration of stochastic volatility model calibration on multi-core CPUs and GPUs using the Xcelerit platform. By adopting a simple dataow programming model, the Xcelerit platform enables the application developer to write sequential, high-level C++ code, without concern for low-level high-performance computing frameworks. This platform provides the portability,exibility and modularity required by application developers. Speedups of up to 30x and 293x are respectively achieved on an Intel Xeon CPU and NVIDIA Tesla K40 GPU, compared to a sequential CPU implementation. The Xcelerit platform implementation is further shown to be equivalent in performance to a low-level CUDA version. Overall, we are able to reduce the entire calibration process time of the sequential implementation from 6; 189 seconds to 183:8 and 17:8 seconds on the CPU and GPU respectively without requiring the developer to reimplement in low-level high performance computing frameworks.