A Bayesian approach to term structure modeling using heavy‐tailed distributions
Carlos A. Abanto‐Valle, Victor Hugo Lachos, Pulak Ghosh · Applied Stochastic Models in Business and Industry · 2011
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‐tand 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.