Computational tools for comparing asymmetric GARCH models via Bayes factors

Ricardo S. Ehlers

Mathematics and Computers in Simulation (MATCOM), 2012, vol. 82, issue 5, 858-867

Abstract: In this paper we use Markov chain Monte Carlo (MCMC) methods in order to estimate and compare GARCH models from a Bayesian perspective. We allow for possibly heavy tailed and asymmetric distributions in the error term. We use a general method proposed in the literature to introduce skewness into a continuous unimodal and symmetric distribution. For each model we compute an approximation to the marginal likelihood, based on the MCMC output. From these approximations we compute Bayes factors and posterior model probabilities.

Keywords: GARCH; Markov chain Monte Carlo; Metropolis-Hastings; Marginal likelihood (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:82:y:2012:i:5:p:858-867

DOI: 10.1016/j.matcom.2011.12.005

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