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Model order reduction for parametric high dimensional models in the analysis of financial risk

Author

Listed:
  • Andreas Binder

    (MathConsult GmbH, Linz, Austria)

  • Onkar Jadhav

    (MathConsult GmbH, Linz, Austria
    Institute of Mathematics, TU Berlin, Berlin, Germany)

  • Volker Mehrmann

    (Institute of Mathematics, TU Berlin, Berlin, Germany)

Abstract

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.

Suggested Citation

  • Andreas Binder & Onkar Jadhav & Volker Mehrmann, 2020. "Model order reduction for parametric high dimensional models in the analysis of financial risk," Papers 2002.11976, arXiv.org, revised Jul 2020.
    • Handle: RePEc:arx:papers:2002.11976
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    References listed on IDEAS

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    1. Vasicek, Oldrich, 1977. "An equilibrium characterization of the term structure," Journal of Financial Economics, Elsevier, vol. 5(2), pages 177-188, November.
    2. Vasicek, Oldrich Alfonso, 1977. "Abstract: An Equilibrium Characterization of the Term Structure," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(4), pages 627-627, November.
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