Machine learning analysis and modeling of interest rate curves
Détails
ID Serval
serval:BIB_B6C29AE21BC4
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Machine learning analysis and modeling of interest rate curves
Titre de la conférence
European Symposium on Artificial Neural Networks: Computational intelligence and machine learning, Bruges, Belgium
ISBN
2-930307-10-2
Statut éditorial
Publié
Date de publication
2010
Pages
47-52
Langue
anglais
Résumé
The present research deals with the review of the analysis and modeling
of Swiss franc interest rate curves (IRC) by using unsupervised (SOM,
Gaussian Mixtures) and supervised machine (MLP) learning algorithms.
IRC are considered as objects embedded into different feature spaces:
maturities; maturity-date, parameters of Nelson-Siegel model (NSM).
Analysis of NSM parameters and their temporal and clustering structures
helps to understand the relevance of model and its potential use
for the forecasting. Mapping of IRC in a maturity-date feature space
is presented and analyzed for the visualization and forecasting purposes.
of Swiss franc interest rate curves (IRC) by using unsupervised (SOM,
Gaussian Mixtures) and supervised machine (MLP) learning algorithms.
IRC are considered as objects embedded into different feature spaces:
maturities; maturity-date, parameters of Nelson-Siegel model (NSM).
Analysis of NSM parameters and their temporal and clustering structures
helps to understand the relevance of model and its potential use
for the forecasting. Mapping of IRC in a maturity-date feature space
is presented and analyzed for the visualization and forecasting purposes.
Création de la notice
25/11/2013 17:18
Dernière modification de la notice
20/08/2019 15:25
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