Optimal stopping via reinforced regression

Denis Belomestny, John Schoenmakers, Vladimir Spokoiny and Bakhyt Zharkynbay

Papers from arXiv.org

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.

Date: 2018-08, Revised 2019-07
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1808.02341

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