英文标题：
《Mixing LSMC and PDE Methods to Price Bermudan Options》
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作者：
David Farahany, Kenneth Jackson, Sebastian Jaimungal
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最新提交年份：
2020
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英文摘要：
We develop a mixed least squares Monte Carlo-partial differential equation (LSMC-PDE) method for pricing Bermudan style options on assets whose volatility is stochastic. The algorithm is formulated for an arbitrary number of assets and volatility processes and we prove the algorithm converges almost surely for a class of models. We also discuss two methods to improve the algorithm\'s computational complexity. Our numerical examples focus on the single ($2d$) and multi-dimensional ($4d$) Heston models and we compare our hybrid algorithm with classical LSMC approaches. In each case, we find that the hybrid algorithm outperforms standard LSMC in terms of estimating prices and optimal exercise boundaries.
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中文摘要：
我们开发了一种混合最小二乘蒙特卡罗偏微分方程（LSMC-PDE）方法，用于对波动率随机的资产进行百慕大式期权定价。该算法适用于任意数量的资产和波动过程，我们证明了该算法对于一类模型几乎肯定收敛。我们还讨论了两种提高算法计算复杂度的方法。我们的数值例子集中在单个（$2d$）和多维（$4d$）Heston模型上，并将我们的混合算法与经典的LSMC方法进行了比较。在每一种情况下，我们发现混合算法在估计价格和最优行使边界方面都优于标准LSMC。
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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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