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Mapping swap rate projections on bond yields considering cointegration: an example for the use of neural networks in stress testing exercises

  • S.I. : Networks and Risk Management
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Abstract

This paper discusses the application of techniques of business analytics in the banking industry examining stress tests in the context of financial risk management. We focus on the use of neural networks in combination with techniques of cointegration analysis to map swap rate projections derived from given scenarios (e.g., a certain stress scenario from the EBA/ECB 2016 EU-wide stress test) on other relevant interest rates in order to ensure that contingent projections for these time series are produced and used in the process of stress testing.

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Authors and Affiliations

  1. Institute of Information Systems Research, Leibniz University Hannover, Hannover, Germany

    Nikolas Stege

  2. Ernst & Young GmbH, Hannover, Germany

    Nikolas Stege

  3. Leuphana University Lüneburg, Lüneburg, Germany

    Christoph Wegener

  4. Nord/LB, Hannover, Germany

    Tobias Basse & Frederik Kunze

Authors
  1. Nikolas Stege
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  2. Christoph Wegener
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  3. Tobias Basse
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  4. Frederik Kunze
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Correspondence to Nikolas Stege.

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An earlier version of this paper with the title “Mapping Interest Rate Projections Using Neural Networks Under Cointegration - An Application from Stress Testing Approaches” was presented at the International Conference on Internet of Things and Machine Learning 2017 in Liverpool. We are grateful to the participants of this conference for their helpful comments and suggestions which have helped to produce this extended version. The conference paper is available in ACM Digital Library, ISBN: 978-1-4503-5243-7.

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Stege, N., Wegener, C., Basse, T. et al. Mapping swap rate projections on bond yields considering cointegration: an example for the use of neural networks in stress testing exercises. Ann Oper Res 297, 309–321 (2021). https://doi.org/10.1007/s10479-020-03762-x

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  • DOI: https://doi.org/10.1007/s10479-020-03762-x

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