Communications in Mathematical Sciences
Volume 18 (2020)
Optimal stopping via reinforced regression
Pages: 109 – 121
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.
Monte Carlo, optimal stopping, regression, reinforcement
2010 Mathematics Subject Classification
60H35, 62P05, 65C05
This work was supported by the Russian Science Foundation (RSF) grant 19-71-30020 and by the Excellence Cluster Math+ Berlin, project AA4-2.
Accepted 3 September 2019
Published 1 April 2020