2016
DOI: 10.1007/s00222-016-0692-2
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Optimal transport and Skorokhod embedding

Abstract: The Skorokhod embedding problem is to represent a given probability as the distribution of Brownian motion at a chosen stopping time. Over the last 50 years this has become one of the important classical problems in probability theory and a number of authors have constructed solutions with particular optimality properties. These constructions employ a variety of techniques ranging from excursion theory to potential and PDE theory and have been used in many different branches of pure and applied probability. We… Show more

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Cited by 125 publications
(288 citation statements)
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“…The above dual formulation (2.6) has been initially provided and proved by [1] in the one marginal case (n = 1), under the condition that (ω, θ) → Φ(ω θ∧· , θ) is bounded from above and upper semicontinuous. When n = 1, our duality results hold under more general conditions: Φ is non-anticipative, bounded from above, and θ → Φ(ω θ∧· , θ) is u.s.c.…”
Section: More Discussion and Examplesmentioning
confidence: 99%
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“…The above dual formulation (2.6) has been initially provided and proved by [1] in the one marginal case (n = 1), under the condition that (ω, θ) → Φ(ω θ∧· , θ) is bounded from above and upper semicontinuous. When n = 1, our duality results hold under more general conditions: Φ is non-anticipative, bounded from above, and θ → Φ(ω θ∧· , θ) is u.s.c.…”
Section: More Discussion and Examplesmentioning
confidence: 99%
“…The dual problem in [1,2] uses a pathwise formulation. Moreover, instead of the stochastic integral (H · B) in our case, they use martingales which are continuous in (t, ω) in the dual formulation.…”
Section: More Discussion and Examplesmentioning
confidence: 99%
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“…We provide an alternative proof of the monotonicity principle for the optimal Skorokhod embedding problem established in Beiglböck, Cox & Huesmann [2]. Our proof is based on the adaptation of the Monge-Kantorovich duality in our context, a delicate application of the optional cross-section theorem, and a clever conditioning argument introduced in [2]. …”
mentioning
confidence: 99%