Bayesian non-linear modellings of the short term US interest rate: the help of non-parametric tools
AbstractThis paper is concerned with the empirical investigation of models of the US short term interest rate, using a mixture of classical non-parametric methods and of Bayesian parametric methods. The shape of the drift and volatility functions of the usual diffusion equation are first investigated using a preliminary non-parametric analysis. The paper then develops a Bayesian method for comparing models which is based on the ability of a model to minimise the Hellinger distance between the posterior predictive density and the density of the observed sample. A discretisation of the usual diffusion equation is estimated with different parameterisations which range from variants of the constant elasticity of variance model to various switching models which draw their justifications from the preliminary non-parametric analysis. The paper concludes by some implications for the term structure. It appears that a model good at reproducing the data density is not necessarily the best for simulating the yield curve.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2000038.
Date of creation: 00 Aug 2000
Date of revision:
Contact details of provider:
Postal: Voie du Roman Pays 34, 1348 Louvain-la-Neuve (Belgium)
Fax: +32 10474304
Web page: http://www.uclouvain.be/core
More information through EDIRC
Bayesian econometrics; time series; non-parametric analysis; model evaluation; non-linear modelling; interest rates; term structure.;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- LUBRANO, Michel, 1998.
"Smooth transition GARCH models: a Bayesian perspective,"
CORE Discussion Papers
1998066, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Michel LUBRANO, 2001. "Smooth Transition Garch Models : a Baysian Perspective," Discussion Papers (REL - Recherches Economiques de Louvain) 2001032, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
- Lubrano, M., 1999. "Smooth Transition GARCH Models: a Bayesian perspective," G.R.E.Q.A.M. 99a49, Universite Aix-Marseille III.
- Gray, Stephen F., 1996.
"Modeling the conditional distribution of interest rates as a regime-switching process,"
Journal of Financial Economics,
Elsevier, vol. 42(1), pages 27-62, September.
- Tom Doan, . "RATS programs to replicate Gray's 1996 Regime Switching GARCH paper," Statistical Software Components RTZ00080, Boston College Department of Economics.
- Pfann, Gerard A. & Schotman, Peter C. & Tschernig, Rolf, 1996.
"Nonlinear interest rate dynamics and implications for the term structure,"
Journal of Econometrics,
Elsevier, vol. 74(1), pages 149-176, September.
- G. Pfann & P. Schotman & R. Tschernig, 1994. "Nonlinear Interest Rate Dynamics and Implications for the Term Structure," SFB 373 Discussion Papers 1994,43, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- Lubrano, M., 1998.
"Bayesian Analysis of Nonlinear Time Series Models with a Threshold,"
98a13, Universite Aix-Marseille III.
- Lubrano, M., 1996. "Bayesian Analysis of Nonlinear Time Series Models with Threshold," G.R.E.Q.A.M. 96a12, Universite Aix-Marseille III.
- Shiller, Robert J, 1979. "The Volatility of Long-Term Interest Rates and Expectations Models of the Term Structure," Journal of Political Economy, University of Chicago Press, vol. 87(6), pages 1190-1219, December.
- Koedijk, C.G. & Nissen, F. & Schotman, P.C. & Wolff, C.C.P., 1997. "The dynamics of short-term interest rate volatility reconsidered," Open Access publications from Tilburg University urn:nbn:nl:ui:12-3108628, Tilburg University.
- Jiang, George J. & Knight, John L., 1997. "A Nonparametric Approach to the Estimation of Diffusion Processes, With an Application to a Short-Term Interest Rate Model," Econometric Theory, Cambridge University Press, vol. 13(05), pages 615-645, October.
- repec:fth:louvco:9866 is not listed on IDEAS
- Petros Dellaportas & David G. T. Denison & Chris Holmes, 2007. "Flexible Threshold Models for Modelling Interest Rate Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 419-437.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alain GILLIS).
If references are entirely missing, you can add them using this form.