Statistical Papers

, Volume 47, Issue 4, pp 525–549 | Cite as

A comparison of Bayesian model selection based on MCMC with an application to GARCH-type models

  • Tatiana Miazhynskaia
  • Georg Dorffner


This paper presents a comprehensive review and comparison of five computational methods for Bayesian model selection, based on MCMC simulations from posterior model parameter distributions. We apply these methods to a well-known and important class of models in financial time series analysis, namely GARCH and GARCH-t models for conditional return distributions (assuming normal and t-distributions). We compare their performance with the more common maximum likelihood-based model selection for simulated and real market data. All five MCMC methods proved reliable in the simulation study, although differing in their computational demands. Results on simulated data also show that for large degrees of freedom (where the t-distribution becomes more similar to a normal one), Bayesian model selection results in better decisions in favor of the true model than maximum likelihood. Results on market data show the instability of the harmonic mean estimator and reliability of the advanced model selection methods.

Key words

Bayesian inference Bayesian model selection GARCH models Markov Chain Monte Carlo (MCMC) model likelihood 


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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Tatiana Miazhynskaia
    • 1
  • Georg Dorffner
    • 2
  1. 1.Austrian Research Institute for Artificial IntelligenceViennaAustria
  2. 2.Austrian Research Institute for Artificial Intelligence and Department of Medical Cybernetics and Artificial Intelligence, Center for Brain ResearchMedical University of ViennaViennaAustria

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