英文标题：
《Dynamic programming for optimal stopping via pseudo-regression》
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作者：
Christian Bayer, Martin Redmann, John Schoenmakers
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最新提交年份：
2019
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英文摘要：
  We introduce new variants of classical regression-based algorithms for optimal stopping problems based on computation of regression coefficients by Monte Carlo approximation of the corresponding $L^2$ inner products instead of the least-squares error functional. Coupled with new proposals for simulation of the underlying samples, we call the approach \"pseudo regression\". A detailed convergence analysis is provided and it is shown that the approach asymptotically leads to less computational cost for a pre-specified error tolerance, hence to lower complexity. The method is justified by numerical examples.
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中文摘要：
我们引入了基于回归的经典算法的新变体，该算法基于通过相应的$L^2$内积的蒙特卡罗近似计算回归系数，而不是最小二乘误差函数。再加上模拟潜在样本的新建议，我们称这种方法为“伪回归”。文中给出了详细的收敛性分析，结果表明，对于预先指定的误差容限，该方法渐进地减少了计算量，从而降低了复杂性。数值算例验证了该方法的正确性。
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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：Mathematics        数学
二级分类：Probability        概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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