论文标题
关于赫斯顿模型下的离散亚洲和回顾选项的定价
On Pricing of Discrete Asian and Lookback Options under the Heston Model
论文作者
论文摘要
我们提出了一种新的,数据驱动的方法,用于有效定价 - 固定和浮动算术 - 离散的算术亚洲人以及回顾选项,当时基础过程是由Heston Model Dynamics驱动的。本文提出的方法构成了我们以前的工作的扩展,在这种工作中,解决了时间集成随机桥的抽样问题。该模型依赖于七联盟方案,在该方案中,人工神经网络被用来“学习”利用随机搭配点的随机变量的分布。该方法为蒙特卡洛定价提供了强大的程序。此外,在简化但一般的框架中提供了用于期权定价的半分析公式。与经典的蒙特卡洛定价方案相比,该模型可确保高精度和计算时间的缩短数千次。
We propose a new, data-driven approach for efficient pricing of - fixed- and float-strike - discrete arithmetic Asian and Lookback options when the underlying process is driven by the Heston model dynamics. The method proposed in this article constitutes an extension of our previous work, where the problem of sampling from time-integrated stochastic bridges was addressed. The model relies on the Seven-League scheme, where artificial neural networks are employed to "learn" the distribution of the random variable of interest utilizing stochastic collocation points. The method results in a robust procedure for Monte Carlo pricing. Furthermore, semi-analytic formulae for option pricing are provided in a simplified, yet general, framework. The model guarantees high accuracy and a reduction of the computational time up to thousands of times compared to classical Monte Carlo pricing schemes.