A bayesian approach to term structure modeling using heavy-tailed distributions
Document Type
Article
Publication Title
Applied Stochastic Models in Business and industry
Abstract
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.
DOI Link
Publication Date
1-4-2012
Publisher
Wiley
Volume
Vol.28
Issue
Iss.5
Recommended Citation
Abanto-Valle, Carlos Antonio; Lachos, Victor H; and Ghosh, Pulak, "A bayesian approach to term structure modeling using heavy-tailed distributions" (2012). Faculty Publications. 1597.
https://research.iimb.ac.in/fac_pubs/1597
