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|Title:||A Bayesian approach to term structure modeling using heavy-tailed distributions|
|Author:||Abanto-Valle, Carlos Antonio|
Lachos, Victor H.
|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.|
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 n
|Subject:||Métodos MCMC (Estatística)|
Misturas de escala (Estatística)
|Editor:||John Wiley & Sons|
|Citation:||Applied Stochastic Models In Business And Industry. Wiley-blackwell, v. 28, n. 5, n. 430, n. 447, 2012.|
|Appears in Collections:||IMECC - Artigos e Outros Documentos|
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