This document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.Dieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.Belomestny, DenisSchoenmakers, John G.M.Spokoiny, VladimirTavyrikov, Yuri2018-10-112019-06-2820182198-5855https://doi.org/10.34657/2944https://oa.tib.eu/renate/handle/123456789/2696In this note we propose a new approach towards solving numerically optimal stopping problems via boosted regression based Monte Carlo algorithms. The main idea of the method is to boost 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 several numerical examples from finance.application/pdfeng510Optimal stoppingnonlinear regressiondeep learningReport