Abstract
In this paper we consider a method of solving optimal stopping problems in discrete and continuous time based on their dual representation. A novel and generic simulation-based optimization algorithm not involving nested simulations is proposed and studied. The algorithm involves the optimization of a genuinely penalized dual objective functional over a class of adapted martingales. We prove the convergence of the proposed algorithm and demonstrate its efficiency for optimal stopping problems arising in option pricing.
Citation
Denis Belomestny. "Solving optimal stopping problems via empirical dual optimization." Ann. Appl. Probab. 23 (5) 1988 - 2019, October 2013. https://doi.org/10.1214/12-AAP892
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