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October 14, 2026
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Optimal stopping via reinforced regression

Communications in Mathematical Sciences. 2020. Vol. 18. No. 1. P. 109–121.
Belomestny D., Schoenmakers J., Spokoiny V., Zharkynbay B.

In this note we propose a new approach towards solving numerically optimal stopping problems via reinforced regression-based Monte Carlo algorithms. The main idea of the method is to reinforce standard linear regression algorithms in each backward induction step by adding new basis functions based on the previously estimated continuation values. The proposed methodology is illustrated by several numerical examples from mathematical finance.

Priority areas: mathematics
Language: English
Full text
DOI
Keywords: Monte Carlo simulationsoptimal stopping
Publication based on the results of:
Uncertainty quantification in high-dimensional models (2020)
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