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

DOI10.1007/s00362-006-0305-zzbMath1125.62022OpenAlexW2098564516MaRDI QIDQ849878

Tatiana Miazhynskaia, Georg Dorffner

Publication date: 14 November 2006

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://epub.wu.ac.at/586/1/document.pdf

zbMATH Keywords

Bayesian model selectionGARCH modelsMarkov Chain Monte Carlomodel likelihood

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (10)

Application of the full Bayesian significance test to model selection under informative sampling ⋮ A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood ⋮ On marginal likelihood computation in change-point models ⋮ Bayesian model choice of grouped \(t\)-copula ⋮ Bayesian inference of smooth transition autoregressive (STAR)\((k)\)-GARCH\((l, m)\) models ⋮ Neural Network Models for Conditional Distribution Under Bayesian Analysis ⋮ Bayesian testing for non-linearity in volatility modeling ⋮ A Stochastic Simulation Approach to Model Selection for Stochastic Volatility Models ⋮ Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models ⋮ Markov switching asymmetric GARCH model: stability and forecasting

Uses Software

• tsbridge
• Gibbsit

Cites Work

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• Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques*
• Sampling-Based Approaches to Calculating Marginal Densities
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• Marginal Likelihood From the Metropolis–Hastings Output
• Bayes Factors
• Bayesian analysis of switching ARCH models
• Monte Carlo sampling methods using Markov chains and their applications

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