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Fast approximation methods for credit portfolio risk calculations

Author

Listed:
  • Kevin Jakob

    (University of Augsburg)

  • Johannes Churt

    (Basycon Unternehmensberatung GmbH)

  • Matthias Fischer

    (Friedrich-Alexander-Universität Nürnberg)

  • Kim Nolte

    (Basycon Unternehmensberatung GmbH)

  • Yarema Okhrin

    (University of Augsburg)

  • Dirk Sondermann

    (Basycon Unternehmensberatung GmbH)

  • Stefan Wilke

    (Basycon Unternehmensberatung GmbH)

  • Thomas Worbs

    (Basycon Unternehmensberatung GmbH)

Abstract

Credit risk is one of the main risks financial institutions are exposed to. Within the last two decades, simulation-based credit portfolio models became extremely popular and replaced closed-form analytical ones as computers became more powerful. However, especially for non-homogenous and non-granular portfolios, a full simulation of a credit portfolio model is still time consuming, which can be disadvantageous within some use cases like credit pricing or within stress testing situations where results must be available very quickly. For this purpose, we investigate if methods based on artificial intelligence (AI) can be helpful to approximate a credit portfolio model. We compare the performance of AI-based methods within three different use cases with suitable non AI-based regression methods. As a result, we see that AI-based methods can generally capture portfolio characteristics and speed-up calculations but - depending on the specific use case and the availability of training data - they are not necessarily always the best choice. Particularly, considering the time and costs for collecting data and training of the complex algorithms, non-AI-based methods can be as good as or even better than AI-based ones, while requiring less computational effort.

Suggested Citation

  • Kevin Jakob & Johannes Churt & Matthias Fischer & Kim Nolte & Yarema Okhrin & Dirk Sondermann & Stefan Wilke & Thomas Worbs, 2023. "Fast approximation methods for credit portfolio risk calculations," Digital Finance, Springer, vol. 5(3), pages 689-716, December.
    • Handle: RePEc:spr:digfin:v:5:y:2023:i:3:d:10.1007_s42521-023-00097-7
    DOI: 10.1007/s42521-023-00097-7
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Credit risk; AI; Credit portfolio model; Approximation;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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