Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/52728
Type: Artigo de periódico
Title: A Bayesian approach to term structure modeling using heavy-tailed distributions
Author: Abanto-Valle, CA
Lachos, VH
Ghosh, P
Abstract: In this paper, we introduce a robust extension of the three-factor model of Diebold and Li (J. Econometrics, 130: 337364, 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 DieboldLi 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 (c) 2011 John Wiley & Sons, Ltd.
Subject: interest rates
MCMC
scale mixture of normal distributions
state space models
term structure
Country: EUA
Editor: Wiley-blackwell
Rights: fechado
Identifier DOI: 10.1002/asmb.920
Date Issue: 2012
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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