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arXiv · 1808.02341

Optimal stopping via reinforced regression

Denis Belomestny·John Schoenmakers·Vladimir Spokoiny·Bakhyt Zharkynbay
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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.

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Denis Belomestny, John Schoenmakers, Vladimir Spokoiny, Bakhyt Zharkynbay. 2019-07-01. Optimal stopping via reinforced regression. https://arxiv.org/abs/1808.02341

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