Risk measures based on weak optimal transport
Lade...
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2024
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
DOI (zitierfähiger Link)
Internationale Patentnummer
Angaben zur Forschungsförderung
Deutsche Forschungsgemeinschaft (DFG): 317210226
Projekt
Open Access-Veröffentlichung
Sammlungen
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
Quantitative Finance. Taylor & Francis. ISSN 1469-7688. eISSN 1469-7696. Verfügbar unter: doi: 10.1080/14697688.2024.2403540
Zusammenfassung
In this paper, we study convex risk measures with weak optimal transport penalties. In a first step, we show that these risk measures allow for an explicit representation via a nonlinear transform of the loss function. In a second step, we discuss computational aspects related to the nonlinear transform as well as approximations of the risk measures using, for example, neural networks. Our setup comprises a variety of examples, such as classical optimal transport penalties, parametric families of models, divergence risk measures, uncertainty on path spaces, moment constraints, and martingale constraints. In a last step, we show how to use the theoretical results for the numerical computation of worst-case losses in an insurance context and no-arbitrage prices of European contingent claims after quoted maturities in a model-free setting.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
510 Mathematik
Schlagwörter
Risk measure, Weak optimal transport, Neural network, Model uncertainty, Martingale optimal transport
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690
KUPPER, Michael, Max NENDEL, Alessandro SGARABOTTOLO, 2024. Risk measures based on weak optimal transport. In: Quantitative Finance. Taylor & Francis. ISSN 1469-7688. eISSN 1469-7696. Verfügbar unter: doi: 10.1080/14697688.2024.2403540BibTex
@article{Kupper2024-10-02measu-71046,
year={2024},
doi={10.1080/14697688.2024.2403540},
title={Risk measures based on weak optimal transport},
issn={1469-7688},
journal={Quantitative Finance},
author={Kupper, Michael and Nendel, Max and Sgarabottolo, Alessandro}
}RDF
<rdf:RDF
xmlns:dcterms="http://purl.org/dc/terms/"
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:bibo="http://purl.org/ontology/bibo/"
xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
xmlns:foaf="http://xmlns.com/foaf/0.1/"
xmlns:void="http://rdfs.org/ns/void#"
xmlns:xsd="http://www.w3.org/2001/XMLSchema#" >
<rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/71046">
<dc:creator>Kupper, Michael</dc:creator>
<dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/>
<dcterms:abstract>In this paper, we study convex risk measures with weak optimal transport penalties. In a first step, we show that these risk measures allow for an explicit representation via a nonlinear transform of the loss function. In a second step, we discuss computational aspects related to the nonlinear transform as well as approximations of the risk measures using, for example, neural networks. Our setup comprises a variety of examples, such as classical optimal transport penalties, parametric families of models, divergence risk measures, uncertainty on path spaces, moment constraints, and martingale constraints. In a last step, we show how to use the theoretical results for the numerical computation of worst-case losses in an insurance context and no-arbitrage prices of European contingent claims after quoted maturities in a model-free setting.</dcterms:abstract>
<void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
<dcterms:title>Risk measures based on weak optimal transport</dcterms:title>
<dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/39"/>
<dc:creator>Sgarabottolo, Alessandro</dc:creator>
<dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-10-24T08:48:48Z</dc:date>
<bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/71046"/>
<dc:contributor>Sgarabottolo, Alessandro</dc:contributor>
<dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-10-24T08:48:48Z</dcterms:available>
<dc:contributor>Kupper, Michael</dc:contributor>
<dc:language>eng</dc:language>
<foaf:homepage rdf:resource="http://localhost:8080/"/>
<dc:creator>Nendel, Max</dc:creator>
<dcterms:issued>2024-10-02</dcterms:issued>
<dc:contributor>Nendel, Max</dc:contributor>
</rdf:Description>
</rdf:RDF>Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Ja
Online First: Zeitschriftenartikel, die schon vor ihrer Zuordnung zu einem bestimmten Zeitschriftenheft (= Issue) online gestellt werden. Online First-Artikel werden auf der Homepage des Journals in der Verlagsfassung veröffentlicht.
