英文标题：
《Branching Particle Pricers with Heston Examples》
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作者：
Michael A. Kouritzin, Anne MacKay
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最新提交年份：
2019
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英文摘要：
The use of sequential Monte Carlo within simulation for path-dependent option pricing is proposed and evaluated. Recently, it was shown that explicit solutions and importance sampling are valuable for efficient simulation of spot price and volatility, especially for purposes of path-dependent option pricing. The resulting simulation algorithm is an analog to the weighted particle filtering algorithm that might be improved by resampling or branching. Indeed, some branching algorithms are shown herein to improve pricing performance substantially while some resampling algorithms are shown to be less suitable in certain cases. A historical property is given and explained as the distinguishing feature between the sequential Monte Carlo algorithms that work on path-dependent option pricing and those that do not. In particular, it is recommended to use the so-called effective particle branching algorithm within importance-sampling Monte Carlo methods for path-dependent option pricing. All recommendations are based upon numeric comparison of option pricing problems in the Heston model.
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中文摘要：
提出并评价了在路径相关期权定价模拟中使用序贯蒙特卡罗方法。最近的研究表明，显式解和重要性抽样对于有效模拟现货价格和波动率非常有用，尤其是对于路径相关期权定价而言。生成的模拟算法类似于加权粒子滤波算法，可通过重采样或分支进行改进。事实上，本文展示了一些分支算法以显著提高定价性能，而一些重采样算法在某些情况下不太合适。给出了一个历史属性，并解释为适用于路径相关期权定价的顺序蒙特卡罗算法与不适用于路径相关期权定价的顺序蒙特卡罗算法之间的区别。特别是，建议在路径相关期权定价的重要抽样蒙特卡罗方法中使用所谓的有效粒子分支算法。所有建议均基于赫斯顿模型中期权定价问题的数值比较。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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