This paper proposes a new method for pricing American options that uses importance sampling to reduce estimator bias and variance in simulation-and-regression based methods. Our suggested method uses regressions under the importance measure directly, instead of under the nominal measure as is the standard, to determine the optimal early exercise strategy. Our numerical results show that this method successfully reduces the bias plaguing the standard importance sampling method across a wide range of moneyness and maturities, with negligible change to estimator variance. When a low number of paths is used, our method always improves on the standard method and reduces average root mean squared error of estimated option prices by $22.5\%$.

中文翻译：

---

具有重要性抽样和移动回归的美式期权定价

本文提出了一种新的美式期权定价方法，该方法使用重要性抽样来减少基于模拟和回归的方法中的估计偏差和方差。我们建议的方法直接使用重要性度量下的回归，而不是标准的名义度量下，以确定最佳的早期锻炼策略。我们的数值结果表明，该方法成功地减少了标准重要性抽样方法在广泛的货币和期限范围内的偏差，估计方差的变化可以忽略不计。当使用少量路径时，我们的方法总是比标准方法有所改进，并且将估计期权价格的平均均方根误差减少了$22.5\%$.