Solving optimal stopping problems via empirical dual optimization (Q373842)

This paper studies optimal stopping problems in discrete and continuous time. A simulation-based optimization algorithm is presented to solve the problems by the optimization of a genuinely penalized dual objective functional over a class of adapted martingales. A typical feature of the algorithm is that it does not involve nested simulation. Moreover, its convergence and efficiency are proved for problems appearing in option pricing. For example, the related variance can be made arbitrarily small by a proper choice of approximating martingales.