英文标题：
《Explicit Heston Solutions and Stochastic Approximation for
Path-dependent Option Pricing》
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作者：
Michael A. Kouritzin
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最新提交年份：
2018
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英文摘要：
New simulation approaches to evaluating path-dependent options without matrix inversion issues nor Euler bias are evaluated. They employ three main contributions: Stochastic approximation replaces regression in the LSM algorithm; Explicit weak solutions to stochastic differential equations are developed and applied to Heston model simulation; and Importance sampling expands these explicit solutions. The approach complements Heston (1993) and Broadie and Kaya (2006) by handling the case of path-dependence in the option\'s execution strategy. Numeric comparison against standard Monte Carlo methods demonstrate up to two orders of magnitude speed improvement. The general ideas will extend beyond the important Heston setting.
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中文摘要：
评估了评估路径相关选项的新模拟方法，无需矩阵反演问题，也无需Euler偏差。他们采用了三个主要贡献：在LSM算法中，随机近似代替了回归；建立了随机微分方程的显式弱解，并将其应用于Heston模型仿真；重要性抽样扩展了这些显式解。该方法通过处理期权执行策略中的路径依赖情况，补充了Heston（1993）和Broadie and Kaya（2006）。与标准蒙特卡罗方法的数值比较表明，速度提高了两个数量级。总体思路将超越重要的赫斯顿背景。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Pricing of Securities 证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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