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A Bayesian approach to term structure modeling using heavy-tailed distributions. (English) Zbl 1306.62215
Summary: We introduce a robust extension of the three-factor model of F. X. Diebold and C. Li [“Forecasting the term structure of government bond yields”, J. Econom. 130, No. 2, 337–364 (2006; doi:10.3386/w10048)] 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.

MSC:
62P05 Applications of statistics to actuarial sciences and financial mathematics
91G70 Statistical methods; risk measures
Software:
Scythe
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