英文标题：
《Leave-One-Out Least Square Monte Carlo Algorithm for Pricing American
  Options》
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作者：
Jeechul Woo, Chenru Liu, Jaehyuk Choi
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最新提交年份：
2020
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英文摘要：
  The least square Monte Carlo (LSM) algorithm proposed by Longstaff and Schwartz (2001) is widely used for pricing American options. The LSM estimator contains undesirable look-ahead bias, and the conventional technique of removing it necessitates doubling simulations. We present the leave-one-out LSM (LOOLSM) algorithm for efficiently eliminating look-ahead bias. We also show that look-ahead bias is asymptotically proportional to the regressors-to-simulation paths ratio. Our findings are demonstrated with several option examples, including the multi-asset cases that the LSM algorithm significantly overvalues. The LOOLSM method can be extended to other regression-based algorithms improving the LSM method.
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中文摘要：
Longstaff和Schwartz（2001）提出的最小二乘蒙特卡罗（LSM）算法被广泛用于美式期权定价。LSM估计器包含不希望的前瞻偏差，而消除该偏差的传统技术需要加倍模拟。为了有效地消除前瞻性偏差，我们提出了一种留一LSM（LOOLSM）算法。我们还表明，前瞻偏差与回归器与模拟路径的比率渐近成正比。我们的发现通过几个选项示例进行了演示，包括LSM算法明显高估的多资产案例。LOOLSM方法可以扩展到其他基于回归的算法，以改进LSM方法。
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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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一级分类：Quantitative Finance        数量金融学
二级分类：Mathematical Finance        数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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