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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
    1. Klaus Duellmann & Jonathan Küll & Michael Kunisch, 2010. "Estimating asset correlations from stock prices or default rates - which method is superior?," Post-Print hal-00736734, HAL.
    2. Lee, Shih-Cheng & Lin, Chien-Ting & Yang, Chih-Kai, 2011. "The asymmetric behavior and procyclical impact of asset correlations," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2559-2568, October.
    3. Lutz Hahnenstein, 2004. "Calibrating the CreditMetrics™ correlation concept — Empirical evidence from Germany," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 18(4), pages 358-381, December.
    4. Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019. "Pricing Options and Computing Implied Volatilities using Neural Networks," Risks, MDPI, vol. 7(1), pages 1-22, February.
    5. Michael Jacobs Jr, 2010. "An Empirical Study of Exposure at Default," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 1(1), pages 31-59.
    6. repec:srs:journl:jasf:v:1:y:2010:i:1:p:31-59 is not listed on IDEAS
    7. So Yeon Chun & Miguel A. Lejeune, 2020. "Risk-Based Loan Pricing: Portfolio Optimization Approach with Marginal Risk Contribution," Management Science, INFORMS, vol. 66(8), pages 3735-3753, August.
    8. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    9. Jian Huang & Junyi Chai & Stella Cho, 2020. "Deep learning in finance and banking: A literature review and classification," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-24, December.
    10. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    11. Duellmann, Klaus & Küll, Jonathan & Kunisch, Michael, 2010. "Estimating asset correlations from stock prices or default rates--Which method is superior?," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2341-2357, November.
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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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