Wasserstein distance estimates for stochastic integrals by forward-backward stochastic calculus

We prove Wasserstein distance bounds between the probability distributions of stochastic integrals with jumps, based on the integrands appearing in their stochastic integral representations. Our approach does not rely on the Stein equation or on the propagation of convexity property for Markovian se...

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Main Authors: Breton, Jean-Christophe, Privault, Nicolas
Other Authors: School of Physical and Mathematical Sciences
Format: Journal Article
Language:English
Published: 2022
Subjects:
Online Access:https://hdl.handle.net/10356/160989
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author Breton, Jean-Christophe
Privault, Nicolas
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Breton, Jean-Christophe
Privault, Nicolas
author_sort Breton, Jean-Christophe
collection NTU
description We prove Wasserstein distance bounds between the probability distributions of stochastic integrals with jumps, based on the integrands appearing in their stochastic integral representations. Our approach does not rely on the Stein equation or on the propagation of convexity property for Markovian semigroups, and makes use instead of forward-backward stochastic calculus arguments. This allows us to consider a large class of target distributions constructed using Brownian stochastic integrals and pure jump martingales, which can be specialized to infinitely divisible target distributions with finite Lévy measure and Gaussian components.
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spelling ntu-10356/1609892022-08-10T08:04:36Z Wasserstein distance estimates for stochastic integrals by forward-backward stochastic calculus Breton, Jean-Christophe Privault, Nicolas School of Physical and Mathematical Sciences Science::Mathematics Wasserstein Distance Stochastic Integrals We prove Wasserstein distance bounds between the probability distributions of stochastic integrals with jumps, based on the integrands appearing in their stochastic integral representations. Our approach does not rely on the Stein equation or on the propagation of convexity property for Markovian semigroups, and makes use instead of forward-backward stochastic calculus arguments. This allows us to consider a large class of target distributions constructed using Brownian stochastic integrals and pure jump martingales, which can be specialized to infinitely divisible target distributions with finite Lévy measure and Gaussian components. Ministry of Education (MOE) This research is supported by the Ministry of Education, Singapore, under its Tier 2 Grant MOE2016-T2-1-036, and by the French government under its program ANR-11-LABX-0020-01 “Investissements d’Avenir”. 2022-08-10T08:04:36Z 2022-08-10T08:04:36Z 2022 Journal Article Breton, J. & Privault, N. (2022). Wasserstein distance estimates for stochastic integrals by forward-backward stochastic calculus. Potential Analysis, 56(1), 1-20. https://dx.doi.org/10.1007/s11118-020-09874-0 0926-2601 https://hdl.handle.net/10356/160989 10.1007/s11118-020-09874-0 2-s2.0-85089962363 1 56 1 20 en MOE2016-T2-1-036 Potential Analysis © 2020 Springer Nature B.V. All rights reserved.
spellingShingle Science::Mathematics
Wasserstein Distance
Stochastic Integrals
Breton, Jean-Christophe
Privault, Nicolas
Wasserstein distance estimates for stochastic integrals by forward-backward stochastic calculus
title Wasserstein distance estimates for stochastic integrals by forward-backward stochastic calculus
title_full Wasserstein distance estimates for stochastic integrals by forward-backward stochastic calculus
title_fullStr Wasserstein distance estimates for stochastic integrals by forward-backward stochastic calculus
title_full_unstemmed Wasserstein distance estimates for stochastic integrals by forward-backward stochastic calculus
title_short Wasserstein distance estimates for stochastic integrals by forward-backward stochastic calculus
title_sort wasserstein distance estimates for stochastic integrals by forward backward stochastic calculus
topic Science::Mathematics
Wasserstein Distance
Stochastic Integrals
url https://hdl.handle.net/10356/160989
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