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doi:10.22028/D291-30267
Title: | Pathwise Dynamic Programming |
Author(s): | Bender, Christian Gärtner, Christian Schweizer, Nikolaus |
Language: | English |
Title: | Mathematics of Operations Research |
Volume: | 43 |
Issue: | 3 |
Startpage: | 965 |
Endpage: | 995 |
Publisher/Platform: | Institute for Operations Research and the Management Sciences |
Year of Publication: | 2018 |
Publikation type: | Journal Article |
Abstract: | We present a novel method for deriving tight Monte Carlo confidence intervals for solutions of stochastic dynamic programming equations. Taking some approximate solution to the equation as an input, we construct pathwise recursions with a known bias. Suitably coupling the recursions for lower and upper bounds ensures that the method is applicable even when the dynamic program does not satisfy a comparison principle. We apply our method to three nonlinear option pricing problems, pricing under bilateral counterparty risk, under uncertain volatility, and under negotiated collateralization. |
DOI of the first publication: | 10.1287/moor.2017.0891 |
URL of the first publication: | https://pubsonline.informs.org/doi/10.1287/moor.2017.0891 |
Link to this record: | hdl:20.500.11880/28709 http://dx.doi.org/10.22028/D291-30267 |
ISSN: | 1526-5471 0364-765X |
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: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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