A regression-based smoothing spline Monte Carlo algorithm for pricing American options in discrete time

被引：0
作者：
Michael Kohler
机构：
[1] Darmstadt University of Technology,Department of Mathematics
来源：
AStA Advances in Statistical Analysis | 2008年 / 92卷
关键词：
American options; Consistency; Nonparametric regression; Optimal stopping; Rate of convergence; Regression based Monte Carlo methods; Smoothing spline;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
American options in discrete time can be priced by solving optimal stopping problems. This can be done by computing so-called continuation values, which we represent as regression functions defined recursively by using the continuation values of the next time step. We use Monte Carlo to generate data, and then we apply smoothing spline regression estimates to estimate the continuation values from these data. All parameters of the estimate are chosen data dependent. We present results concerning consistency and the estimates’ rate of convergence.
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页码：153 / 178
页数：25
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