Option pricing with the SABR model on the GPU

Yu Tian, Zili Zhu, F. Klebaner, K. Hamza
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引用次数: 12

Abstract

In this paper, we will present our research on the acceleration for option pricing using Monte Carlo techniques on the GPU. We first introduce some basic ideas of GPU programming and then the stochastic volatility SABR model. Under the SABR model, we discuss option pricing with Monte Carlo techniques. In particular, we focus on European option pricing using quasi-Monte Carlo with the Brownian bridge method and American option pricing using the least squares Monte Carlo method. Next, we will study a GPU-based program for pricing European options and a hybrid CPU-GPU program for pricing American options. Finally, we implement our GPU programs, and compare their performance with their CPU counterparts. From our numerical results, around 100× speedup in European option pricing and 10× speedup in American option pricing can be achieved by GPU computing while maintaining satisfactory pricing accuracy.
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基于GPU的SABR模型的期权定价
在本文中,我们将介绍在GPU上使用蒙特卡罗技术的期权定价加速的研究。首先介绍了GPU编程的一些基本思想,然后介绍了随机波动SABR模型。在SABR模型下,我们用蒙特卡罗技术讨论期权定价问题。本文重点研究了欧式期权的准蒙特卡罗定价方法和美式期权的最小二乘蒙特卡罗定价方法。接下来,我们将研究基于gpu的欧式期权定价方案和基于CPU-GPU的美式期权定价方案。最后,我们实现了我们的GPU程序,并将它们与CPU程序的性能进行了比较。从我们的数值结果来看,在保持满意的定价精度的情况下,GPU计算可以使欧式期权定价加速约100倍,美式期权定价加速约10倍。
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Option pricing with the SABR model on the GPU Pricing structured equity products on GPUs Opportunities for shared memory parallelism in financial modeling Accelerating the computation of portfolios of tranched credit derivatives Adding stream processing system flexibility to exploit low-overhead communication systems
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