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Event Description
Adapted optimal transport has emerged to be a useful tool for quantifying distributional uncertainty and the sensitivity of stochastic optimization problems in contexts where the flow of information in time plays a crucial role. While a rich theory has been developed, the collection of explicit examples is still limited. In this talk we study the adapted (by which we mean bicausal) optimal transport between arbitrary real-valued Gaussian processes in discrete time. We characterize the optimal coupling(s) and study the resulting adapted Bures-Wasserstein distance on the space of covariance matrices. Ongoing work with Madhu Gunasingam (U of T).
Event Category
Financial Mathematics Seminar