Please use this identifier to cite or link to this item: doi:10.22028/D291-30266
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Title: Iterative Improvement of Lower and Upper Bounds for Backward SDEs
Author(s): Bender, Christian
Gärtner, Christian
Schweizer, Nikolaus
Language: English
Title: SIAM Journal on Scientific Computing
Volume: 39
Issue: 2
Startpage: B442
Endpage: B466
Publisher/Platform: Society for Industrial and Applied Mathematics (SIAM)
Year of Publication: 2017
Publikation type: Journal Article
Abstract: We introduce a novel numerical approach for a class of stochastic dynamic programs which arise as discretizations of backward stochastic differential equations or semilinear partial differential equations. Solving such dynamic programs numerically requires the approximation of nested conditional expectations, i.e., iterated integrals of previous approximations. Our approach allows us to compute and iteratively improve upper and lower bounds on the true solution, starting from an arbitrary and possibly crude input approximation. We demonstrate the benefits of our approach in a high-dimensional financial application.
DOI of the first publication: 10.1137/16M1081348
URL of the first publication:
Link to this record: hdl:20.500.11880/28708
ISSN: 1095-7197
Date of registration: 17-Feb-2020
Third-party funds sponsorship: Deutsche Forschungsgemeinschaft
Sponsorship ID: BE3933/5-1
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Mathematik
Professorship: MI - Prof. Dr. Christian Bender
Collections:UniBib – Die Universitätsbibliographie

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