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Sensitivity of multiperiod optimization problems in adapted Wasserstein distance

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arxiv 2208.05656 v2 pith:WRLIE2SD submitted 2022-08-11 math.OC math.PRq-fin.MF

Daniel Bartl , Johannes Wiesel This is my paper · ORCID
classification math.OCmath.PRq-fin.MF
keywords problemsadaptedchangesdistanceoptimizationwassersteinanalyzeapproximations
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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.

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  1. A transfer principle for computing the adapted Wasserstein distance between stochastic processes

    math.PR 2025-05 reject novelty 6.0 of 10

    The adapted 2-Wasserstein distance between fractional Brownian motions equals the Hilbert-Schmidt distance between their Molchan-Golosov kernels, attained by the synchronous coupling.

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