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
-
978-952-323-287-7
- Sprache
-
Englisch
- Erschienen in
-
Series: Bank of Finland Research Discussion Papers ; No. 14/2019
- Klassifikation
-
Wirtschaft
Banks; Depository Institutions; Micro Finance Institutions; Mortgages
Neural Networks and Related Topics
Model Evaluation, Validation, and Selection
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Tölö, Eero
- Ereignis
-
Veröffentlichung
- (wer)
-
Bank of Finland
- (wo)
-
Helsinki
- (wann)
-
2019
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Tölö, Eero
- Bank of Finland
Entstanden
- 2019
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