使用图形处理单元来评估美式衍生品

Leon Xing Li, Ren‐Raw Chen
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引用次数: 0

摘要

本文通过蒙特卡罗(MC)模拟和粒子群优化(PSO)方法,将图形处理单元(GPU)计算应用于一个美式期权定价问题。由于MC和PSO的计算都可以矢量化并独立进行,因此可以很容易地在gpu上进行估值。因此,我们可以通过增加MC路径和粒子来提高估值的准确性,而无需花费更多的时间。例如,使用大量的粒子(但分配给gpu),收敛可以在很少的步骤中达到。本文介绍的方法可以扩展到各种各样的外来衍生品或各种衍生品的大型投资组合(称为特征投资组合)。这对交易和风险管理都很有帮助。
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Using the Graphics Processing Unit to Evaluate American-Style Derivatives
In this article, the authors apply graphics processing unit (GPU) computation to an American option pricing problem via Monte Carlo (MC) simulations and particle swarm optimization (PSO). Given that computations in both MC and PSO can be vectorized and made independent, the valuation can be readily performed on GPUs. As a result, we can increase the accuracy of the valuation by increasing MC paths and particles without spending more time. For example, with a large number of particles (but allocated to GPUs), convergence can be reached in very few steps. The method introduced in this article can be extended to a wide variety of exotic derivatives or a large portfolio of diverse derivatives (known as an eigen portfolio). This is helpful in both trading and risk management.
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