Binder, Andreas
Jadhav, Onkar
Mehrmann, Volker
2020-02-27
<p>This 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.</p>
https://doi.org/10.5281/zenodo.3921985
oai:zenodo.org:3921985
eng
Zenodo
https://zenodo.org/communities/romsoc
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3921984
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Financial risk analysis, short-rate models, convection-diffusion reaction equation, finite differencemethod, parametric model order reduction, proper orthogonal decomposition, adaptive greedy sampling, Packaged retail investment and insurance-based products (PRIIPs).
Model order reduction for parametric high dimensional models in the analysis of financial risk
info:eu-repo/semantics/preprint