Machine learning analysis and modeling of interest rate curves
Details
Serval ID
serval:BIB_B6C29AE21BC4
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Machine learning analysis and modeling of interest rate curves
Title of the conference
European Symposium on Artificial Neural Networks: Computational intelligence and machine learning, Bruges, Belgium
ISBN
2-930307-10-2
Publication state
Published
Issued date
2010
Pages
47-52
Language
english
Abstract
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.
Create date
25/11/2013 17:18
Last modification date
20/08/2019 15:25
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