Artikel
Recurrent Artificial Neural Networks (RANN) for forecasting of forward interest rates
There are numerous methods for estimating forward interest rates as well as many studies testing the accuracy of these methods. The approach proposed in this study is similar to the one in previous works in two respects: firstly, a Monte Carlo simulation is used instead of empirical data to circumvent empirical difficulties: and secondly, in this study, accuracy is measured by estimating the forward rates rather than by exploring bond prices. This is more consistent with user objectives. The method presented here departs from the others in that it uses a Recurrent Artificial Neural Network (RANN) as an alternative technique for forecasting forward interest rates. Its performance is compared to that of a recursive method which has produced some of the best results in previous studies for forecasting forward interest rates.
- Language
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Englisch
- Bibliographic citation
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Journal: South African Journal of Business Management ; ISSN: 2078-5976 ; Volume: 31 ; Year: 2000 ; Issue: 4 ; Pages: 137-140 ; Cape Town: African Online Scientific Information Systems (AOSIS)
- Classification
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Management
- Event
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Geistige Schöpfung
- (who)
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Bensaid, Amine
Bouqata, Bouchra
Palliam, Ralph
- Event
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Veröffentlichung
- (who)
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African Online Scientific Information Systems (AOSIS)
- (where)
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Cape Town
- (when)
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2000
- DOI
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doi:10.4102/sajbm.v31i4.744
- Handle
- Last update
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10.03.2025, 11:44 AM CET
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
- Artikel
Associated
- Bensaid, Amine
- Bouqata, Bouchra
- Palliam, Ralph
- African Online Scientific Information Systems (AOSIS)
Time of origin
- 2000
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Cointegrated VARMA models and forecasting US interest rates
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Time-series forecasting through recurrent topology
Forecasting with artificial network models
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