Mapping interest rate projections using neural networks under cointegration

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

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.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Internet of Things and Machine Learning, IML 2017
EditorsHani Hamdan, Faouzi Hidoussi, Djallel Eddine Boubiche
Number of pages5
PublisherAssociation for Computing Machinery, Inc
Publication date17.10.2017
Article numbera13
ISBN (electronic)9781450352437
DOIs
Publication statusPublished - 17.10.2017
Externally publishedYes
Event1st International Conference on Internet of Things and Machine Learning - IML 2017 - John Moores University, Liverpool, United Kingdom
Duration: 17.10.201718.10.2017
Conference number: 1

    Research areas

  • Artificial neural networks, Cointegration, Mapping interest rate projections, Net interest rate income, Risk management
  • Management studies

DOI

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