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Research Article

A Bayesian approach to term structure modeling using heavy-tailed distributions

  1. Carlos Antonio Abanto-Valle1,*,
  2. Victor H. Lachos2,
  3. Pulak Ghosh3

Article first published online: 1 SEP 2011

DOI: 10.1002/asmb.920

Issue

Applied Stochastic Models in Business and Industry

Applied Stochastic Models in Business and Industry

Volume 28, Issue 5, pages 430–447, September/October 2012

Additional Information

How to Cite

Abanto-Valle, C. A., Lachos, V. H. and Ghosh, P. (2012), A Bayesian approach to term structure modeling using heavy-tailed distributions. Appl. Stochastic Models Bus. Ind., 28: 430–447. doi: 10.1002/asmb.920

Author Information

  1. 1

    Department of Statistics, Federal University of Rio de Janeiro, Caixa Postal 68530, CEP: 21945-970, Rio de Janeiro, Brazil

  2. 2

    Department of Statistics, Campinas State University, Campinas, Brazil

  3. 3

    Department of Quantitative Methods and Information Systems, Indian Institute of Management, Bangalore, India

* Carlos Antonio Abanto-Valle, Department of Statistics, Federal University of Rio de Janeiro, Caixa Postal 68530, CEP: 21945-970, Rio de Janeiro, Brazil
E-mail: cabantovalle@im.ufrj.br

Publication History

  1. Issue published online: 12 OCT 2012
  2. Article first published online: 1 SEP 2011
  3. Manuscript Accepted: 1 JUL 2011
  4. Manuscript Revised: 19 APR 2011
  5. Manuscript Received: 9 JUL 2010

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Keywords:

  • interest rates;
  • MCMC;
  • scale mixture of normal distributions;
  • state space models;
  • term structure

In this paper, we introduce a robust extension of the three-factor model of Diebold and Li (J. Econometrics, 130: 337–364, 2006) using the class of symmetric scale mixtures of normal distributions. Specific distributions examined include the multivariate normal, Student-t, slash, and variance gamma distributions. In the presence of non-normality in the data, these distributions provide an appealing robust alternative to the routine use of the normal distribution. Using a Bayesian paradigm, we developed an efficient MCMC algorithm for parameter estimation. Moreover, the mixing parameters obtained as a by-product of the scale mixture representation can be used to identify outliers. Our results reveal that the Diebold–Li models based on the Student-t and slash distributions provide significant improvement in in-sample fit and out-of-sample forecast to the US yield data than the usual normal-based model. Copyright © 2011 John Wiley & Sons, Ltd.

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