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Bayesian Estimation of Generalized Hyperbolic Skewed Student GARCH Models

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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.

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Bibliographic Info

Paper provided by Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland in its series DQE Working Papers with number 16.

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Length: 41 pages
Date of creation: 28 Oct 2011
Date of revision: 09 Jun 2012
Publication status: Published in Computational Statistics and Data Analysis, 2012, vol.56, issue 11, pp. 3035-3054
Handle: RePEc:fri:dqewps:wp0016

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Keywords: Autoregressive conditional heteroskedasticity; Markov chain Monte Carlo; bridge sampling; heavy-tailed skewed distributions; generalized hyperbolic distribution; generalized inverse Gaussian distribution;

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Cited by:
  1. Deniz Erdemlioglu & Sébastien Laurent & Christopher J. Neely, 2012. "Econometric modeling of exchange rate volatility and jumps," Working Papers 2012-008, Federal Reserve Bank of St. Louis.

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