Please use this identifier to cite or link to this item: doi:10.22028/D291-30267
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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 (INFORMS)
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:
Link to this record: hdl:20.500.11880/28709
ISSN: 1526-5471
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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