期权定价的二阶Malliavin权离散和拟蒙特卡罗方法

ERN: Simulation Methods (Topic) Pub Date : 2018-02-06 DOI:10.2139/ssrn.3012898

T. Yamada, Kenta Yamamoto

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引用次数: 5

摘要

本文给出了随机微分方程期望的一种有效的二阶离散化格式。我们引入了由布朗运动的简单多项式给出的智能马利文权重，作为Yamada(2017)方案的改进。提出了一种新的拟蒙特卡罗模拟方法，以获得一种有效的期权定价方案。最后给出了SABR模型的数值算例，说明了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Second Order Discretization with Malliavin Weight and Quasi-Monte Carlo Method for Option Pricing

This paper shows an efficient second order discretization scheme of expectations of stochastic differential equations. We introduce smart Malliavin weight which is given by a simple polynomials of Brownian motions as an improvement of the scheme of Yamada (2017). A new quasi Monte Carlo simulation is proposed to attain an efficient option pricing scheme. Numerical examples for the SABR model are shown to illustrate the validity of the scheme.

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ERN: Simulation Methods (Topic)

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