Optimal stopping via reinforced regression
Numerical Analysis
2019-07-02 v3 Numerical Analysis
Computational Finance
Machine Learning
Abstract
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.
Cite
@article{arxiv.1808.02341,
title = {Optimal stopping via reinforced regression},
author = {Denis Belomestny and John Schoenmakers and Vladimir Spokoiny and Bakhyt Zharkynbay},
journal= {arXiv preprint arXiv:1808.02341},
year = {2019}
}