Optimal stopping via reinforced regression
This work addresses a computational challenge in optimal stopping for researchers in mathematical finance, but it appears incremental as it builds on existing regression-based methods.
The authors tackled the numerical solution of optimal stopping problems by introducing a reinforced regression Monte Carlo method that enhances standard linear regression with new basis functions derived from previously estimated continuation values, and demonstrated its application in a mathematical finance example.
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 previously estimated continuation values. The proposed methodology is illustrated by a numerical example from mathematical finance.