Sensitivity of Multiperiod Optimization Problems with Respect to the Adapted Wasserstein Distance.

We analyze the effect of small changes in the underlying probabilistic model on the value of multi -period stochastic optimization problems and optimal stopping problems. We work in finite discrete time and measure these changes with the adapted Wasserstein distance. We prove explicit first-order approximations for both problems. Expected utility maximization is discussed as a special case.