Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-14699
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dc.contributor.authorBinder, Andreas-
dc.contributor.authorJadhav, Onkar-
dc.contributor.authorMehrmann, Volker-
dc.date.accessioned2021-12-17T10:16:17Z-
dc.date.available2021-12-17T10:16:17Z-
dc.date.issued2020-02-27-
dc.identifier.issn2197-8085-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/15926-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-14699-
dc.description.abstractThis paper presents a model order reduction (MOR) approach for high dimensional problems in the analysis of financial risk. To understand the financial risks and possible outcomes, we have to perform several thousand simulations of the underlying product. These simulations are expensive and create a need for efficient computational performance. Thus, to tackle this problem, we establish a MOR approach based on a proper orthogonal decomposition (POD) method. The study involves the computations of high dimensional parametric convection-diffusion reaction partial differential equations (PDEs). POD requires to solve the high dimensional model at some parameter values to generate a reduced-order basis. We propose an adaptive greedy sampling technique based on surrogate modeling for the selection of the sample parameter set that is analyzed, implemented, and tested on the industrial data. The results obtained for the numerical example of a floater with cap and floor under the Hull-White model indicate that the MOR approach works well for short-rate models.en
dc.description.sponsorshipEC/H2020/765374/EU/Reduced Order Modelling, Simulation and Optimization of Coupled Systems/ROMSOCen
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc510 Mathematiken
dc.subject.otherfinancial risk analysisen
dc.subject.othershort-rate modelsen
dc.subject.otherconvection-diffusion reaction equationen
dc.subject.otherfinite difference methoden
dc.subject.otherparametric model order reductionen
dc.subject.otherproper orthogonal decompositionen
dc.subject.otheradaptive greedy samplingen
dc.subject.otherpackaged retail investment and insurance-based productsen
dc.subject.otherPRIIPsen
dc.titleModel order reduction for parametric high dimensional models in the analysis of financial risken
dc.typeResearch Paperen
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
tub.series.issuenumber2020, 03en
tub.series.namePreprint-Reihe des Instituts für Mathematik, Technische Universität Berlinen
dc.type.versionsubmittedVersionen
tub.affiliationFak. 2 Mathematik und Naturwissenschaften » Inst. Mathematikde
tub.subject.msc200035L70 Nonlinear second-order PDE of hyperbolic typeen
tub.subject.msc200065M06 Finite difference methodsen
tub.subject.msc200062P05 Applications to actuarial sciences and financial mathematicsen
tub.subject.msc200091-08 Computational methodsen
Appears in Collections:Technische Universität Berlin » Publications

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