Arbeitspapier
Predicting systemic financial crises with recurrent neural networks
We consider predicting systemic financial crises one to five years ahead using recurrent neural networks. The prediction performance is evaluated with the Jorda-Schularick-Taylor dataset, which includes the crisis dates and relevant macroeconomic series of 17 countries over the period 1870-2016. Previous literature has found simple neural network architectures to be useful in predicting systemic financial crises. We show that such predictions can be greatly improved by making use of recurrent neural network architectures, especially suited for dealing with time series input. The results remain robust after extensive sensitivity analysis.
- ISBN
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978-952-323-287-7
- Language
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Englisch
- Bibliographic citation
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Series: Bank of Finland Research Discussion Papers ; No. 14/2019
- Classification
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Wirtschaft
Banks; Depository Institutions; Micro Finance Institutions; Mortgages
Neural Networks and Related Topics
Model Evaluation, Validation, and Selection
- Event
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Geistige Schöpfung
- (who)
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Tölö, Eero
- Event
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Veröffentlichung
- (who)
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Bank of Finland
- (where)
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Helsinki
- (when)
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2019
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Tölö, Eero
- Bank of Finland
Time of origin
- 2019
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