Optimal stopping via reinforced regression

Denis Belomestny, John Schoenmakers, Vladimir Spokoiny, Bakhyt Zharkynbay

Research output: Contribution to journalArticlepeer-review

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 the previously estimated continuation values. The proposed methodology is illustrated by several numerical examples from mathematical finance.

Original languageEnglish
Pages (from-to)109-121
Number of pages13
JournalCommunications in Mathematical Sciences
Volume18
Issue number1
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Monte carlo
  • Optimal stopping
  • Regression
  • Reinforcement

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