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

作者：Abanto Valle Carlos Antonio^*; Lachos Victor H; Ghosh Pulak

来源：Applied Stochastic Models in Business and Industry, 2012, 28(5): 430-447.

DOI：10.1002/asmb.920

• 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: 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.

• 出版日期2012-10