WIAS Preprint No. 2530, (2018)

Optimal stopping via deeply boosted backward regression


  • Belomestny, Denis
  • Schoenmakers, John G. M.
    ORCID: 0000-0002-4389-8266
  • Spokoiny, Vladimir
    ORCID: 0000-0002-2040-3427
  • Tavyrikov, Yuri

2010 Mathematics Subject Classification

  • 60G40 65C05 62J02


  • Optimal stopping, nonlinear regression, deep learning




In 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.

Appeared in

  • Comm. Math. Sci., 18 (2020), pp. 109--121, with different title ``Optimal stopping via reinforced regression'' and different 4th author ``Bakhyt Zharkynbay ", DOI 10.4310/CMS.2020.v18.n1.a5 .

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