Bayesian estimation of generalized hyperbolic skewed student GARCH models

Philippe Deschamps

Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3035-3054

Abstract: Efficient posterior simulators for two GARCH models with generalized hyperbolic disturbances are presented. The first model, GHt-GARCH, is a threshold GARCH with a skewed and heavy-tailed error distribution; in this model, the latent variables that account for skewness and heavy tails are identically and independently distributed. The second model, ODLV-GARCH, is formulated in terms of observation-driven latent variables; it automatically incorporates a risk premium effect. Both models nest the ordinary threshold t-GARCH as a limiting case. The GHt-GARCH and ODLV-GARCH models are compared with each other and with the threshold t-GARCH using five publicly available asset return data sets, by means of Bayes factors, information criteria, and classical forecast evaluation tools. The GHt-GARCH and ODLV-GARCH models both strongly dominate the threshold t-GARCH, and the Bayes factors generally favor GHt-GARCH over ODLV-GARCH. A Markov switching extension of GHt-GARCH is also presented. This extension is found to be an empirical improvement over the single-regime model for one of the five data sets.

Keywords: Autoregressive conditional heteroskedasticity; Markov chain Monte Carlo; Bridge sampling; Heavy-tailed skewed distributions; Generalized hyperbolic distribution; Generalized inverse Gaussian distribution (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:3035-3054

DOI: 10.1016/j.csda.2011.10.021

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