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Published in:
Processing Nonlinearities Read chapter Read first chapter

2019 | OriginalPaper | Chapter

22. Yield Curve Estimation Under Extreme Conditions: Do RBF Networks Perform Better?

Authors : Alessia Cafferata, Pier Giuseppe Giribone, Marco Neffelli, Marina Resta

Published in: Neural Advances in Processing Nonlinear Dynamic Signals

Publisher: Springer International Publishing

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Abstract

In this paper we test the capability of Radial Basis Function (RBF) networks to fit the yield curve under extreme conditions, namely in case of either negative spot interest rates, or high volatility. In particular, we compare the performances of conventional parametric models (Nelson–Siegel, Svensson and de Rezende–Ferreira) to those of RBF networks to fit term structure curves. To such aim, we consider the Euro Swap–EUR003M Euribor, and the USDollar Swap (USD003M) curves, on two different release dates: on December 30th 2004 and 2016, respectively, i.e. under very different market situations, and we examined the various ability of the above–cited methods in fitting them. Our results show that while in general conventional methods fail in adapting to anomalies, such as negative interest rates or big humps, RBF nets provide excellent statistical performances, thus confirming to be a very flexible tool adapting to every market’s condition.

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Metadata
Title
Yield Curve Estimation Under Extreme Conditions: Do RBF Networks Perform Better?
Authors
Alessia Cafferata
Pier Giuseppe Giribone
Marco Neffelli
Marina Resta
Copyright Year
2019
Book
Neural Advances in Processing Nonlinear Dynamic Signals

Print ISBN: 978-3-319-95097-6

Electronic ISBN: 978-3-319-95098-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-95098-3_22

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