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 circumve...

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Main Authors: Amine Bensaid, Bouchra Bouqata, Ralph Palliam
Format: Article
Language:English
Published: AOSIS 2000-12-01
Series:South African Journal of Business Management
Online Access:https://sajbm.org/index.php/sajbm/article/view/744
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author Amine Bensaid
Bouchra Bouqata
Ralph Palliam
author_facet Amine Bensaid
Bouchra Bouqata
Ralph Palliam
author_sort Amine Bensaid
collection DOAJ
description 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.
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spelling doaj.art-e73f2a5cd88a4aea8443838982e4b45d2022-12-22T02:24:50ZengAOSISSouth African Journal of Business Management2078-55852078-59762000-12-0131413714010.4102/sajbm.v31i4.744464Recurrent Artificial Neural Networks (RANN) for forecasting of forward interest ratesAmine Bensaid0Bouchra Bouqata1Ralph Palliam2School of Science and Engineering, Al-Akahawayn UniversitySchool of Science and Engineering, Al-Akahawayn UniversitySchool of Business Administration, Al-Akahawayn UniversityThere 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.https://sajbm.org/index.php/sajbm/article/view/744
spellingShingle Amine Bensaid
Bouchra Bouqata
Ralph Palliam
Recurrent Artificial Neural Networks (RANN) for forecasting of forward interest rates
South African Journal of Business Management
title Recurrent Artificial Neural Networks (RANN) for forecasting of forward interest rates
title_full Recurrent Artificial Neural Networks (RANN) for forecasting of forward interest rates
title_fullStr Recurrent Artificial Neural Networks (RANN) for forecasting of forward interest rates
title_full_unstemmed Recurrent Artificial Neural Networks (RANN) for forecasting of forward interest rates
title_short Recurrent Artificial Neural Networks (RANN) for forecasting of forward interest rates
title_sort recurrent artificial neural networks rann for forecasting of forward interest rates
url https://sajbm.org/index.php/sajbm/article/view/744
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