nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

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

verfasst von : Alessia Cafferata, Pier Giuseppe Giribone, Marco Neffelli, Marina Resta

Erschienen in: Neural Advances in Processing Nonlinear Dynamic Signals

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel An Improved PSO for Flexible Parameters Identification of Lithium Cells Equivalent Circuit Models

Nächstes Kapitel New Challenges in Pension Industry: Proposals of Personal Pension Products

Ait-Sahalia, Y.: Nonparametric pricing of interest rate derivative securities. Econometrica 64, 527–560 (1996)CrossRef

Annaert, J., Claes, A.G., De Ceuster, M.J.K., Zhang, H.: Estimating the spot rate curve using the Nelson–Siegel model. a ridge regression approach. Int. Rev. Econ. Financ. 27, 482–496 (2013)CrossRef

Barunik, J., Malinska, B.: Forecasting the term structure of crude oil futures prices with neural networks. Appl. Energy 164, 366–379 (2016)CrossRef

Bonnans, J.F., Gilbert, J.Ch., Lemarchal, C., Sagastizbal, C.A.: Numerical Optimization, Theoretical and Numerical Aspects. 2nd edn. Springer (2006)

Bose, S.K., Sethuraman, J., Raipet, S.: Forecasting the term structure of interest rates using neural networks. In Kamruzzaman, J., Begg, R.K., Sarker, R.K. (eds.) Artificial Neural Networks in Finance and Manufacturing, chap. 8, pp. 124–138. IGI Global (2006)

Broomhead, D.S., Lowe, D.: Multivariate function interpolation and adaptive networks. Complex Syst. 2, 321–355 (1988)MATH

Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATH

Cottrell, M., de Bodt, E., Grégoire, P.: Interest rates structure dynamics: a non parametric approach. In: Refenes, A.N., Burgess, A.N., Moody, J.E. (eds.) Decision Technologies for Computational Finance: Proceedings of the Fifth International Conference Computational Finance, pp. 259–266. Springer US, Boston (1998)CrossRef

De Pooter, M.: Examining the Nelson–Siegel class of term structure models. Technical Report 043/4, Tinbergen Institute (2007)

10.

de Rezende, R.B., Ferreira, M.S.: Modeling and forecasting the yield curve by an extended nelson-siegel class of models: a quantile autoregression approach. J. Forecast. 32, 111–123 (2013)MathSciNetCrossRef

11.

Diebold, F., Li, C.: Forecasting the term structure of government bond yields. J. Econ. 130(2), 337–364 (2006)MathSciNetCrossRef

12.

Diebold, F., Rudebusch, G.D.: The Dynamic Nelson–Siegel Approach to Yield Curve Modeling and Forecasting. Princeton University Press (2013)

13.

Fama, E., Bliss, R.R.: The information in long maturity forward rates. Am. Econ. Rev. 77(4), 680–692 (1987)

14.

Gilli, M., Grosse, S., Schumann, E.: Calibrating the Nelson-Siegel-Svensson model. Technical report, COMISEF (2010)

15.

Gogas, P., Papadimitriou, T., Matthaiou, M., Chrysanthidou, E.: Yield curve and recession forecasting in a machine learning framework. Comput. Econ. 45, 635–645 (2015)CrossRef

16.

Golbabai, A., Ahmadian, D., Milev, M.: Radial basis functions with application to finance: american put option under jump diffusion. Math. Comput. Model. 55, 1354–1362 (2012)MathSciNetCrossRef

17.

Grbac, Z., Runggaldier, W.J.: Interest Rate Modeling: Post-Crisis Challenges and Approaches. Springer (2014)

18.

Henrard, M.: Interest Rate Modelling in the Multi–curve Framework. Palgrave McMillan (2014)

19.

Joseph, A., Larrain, M., Singh, E.: Predictive ability of the interest rate spread using neural networks. Procedia Comput. Sci. 6, 207–212 (2011)CrossRef

20.

Larsson, E., Gomes, S.M., Heryudono, A., Safdari-Vaighani, A.: Radial basis function methods in computational finance. In: Proceedings of the 13th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2013 (2013)

21.

Nelson, C., Siegel, A.F.: Parsimonious modeling of yield curves. J. Bus. 60, 473–489 (1987)CrossRef

22.

Rosadi, D., Nugraha, Y.A., Dewi, R.K.: Forecasting the Indonesian government securities yield curve using neural networks and vector autoregressive model. Technical report, Department of Mathematics, Gadjah Mada University, Indonesia (2011)

23.

Sambasivan, R., Das, S.: A statistical machine learning approach to yield curve forecasting. Technical report, Chennai Mathematical Institute (2017)

24.

Svensson, L.E.O.: Estimating the term structure of interest rates for monetary policy analysis. Scand. J. Econ. 98, 163–183 (1996)CrossRef

25.

Tappinen, J.: Interest rate forecasting with neural networks. Technical Report 170, Government Institute for Economic Research (1998)

26.

Vasicek, O.: An equilibrium characterization of the term structure. J. Financ. Econ. 5, 177–188 (1977)CrossRef

Titel: Yield Curve Estimation Under Extreme Conditions: Do RBF Networks Perform Better?
verfasst von: Alessia Cafferata
Pier Giuseppe Giribone
Marco Neffelli
Marina Resta
Verlag: Springer International Publishing
Buch: Neural Advances in Processing Nonlinear Dynamic Signals
Print ISBN: 978-3-319-95097-6

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

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-95098-3_22

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner