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Modelos GARCH Bayesianos: Métodos Aproximados e Aplicações

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  • Migon, Helio S.
  • Mazucheli, Josmar

Abstract

The class of GARCH models is briefly revised and those models reformulated as dynamic Bayesian models. Gaussian quadrature technique and Laplace approximation are used to estimate both static and dynamic GARCH models of moderate dimension. The implemented algorithms are validated using artificially generated data. Four real return time series were analized and the performance of the different models and estimation methods accessed through their predictive capability and, also, comparing the non-observed volatilities with the square af the returns, its natural proxy.

Suggested Citation

  • Migon, Helio S. & Mazucheli, Josmar, 1999. "Modelos GARCH Bayesianos: Métodos Aproximados e Aplicações," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 19(1), May.
    • Handle: RePEc:sbe:breart:v:19:y:1999:i:1:a:2794
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    Cited by:

    1. Fajardo, José & Farias, Aquiles, 2004. "Generalized Hyperbolic Distributions and Brazilian Data," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 24(2), November.
    2. Fajardo, J. & Cajueiro, D. O., 2003. "Volatility Estimation and Option Pricing with Fractional Brownian Motion," Finance Lab Working Papers flwp_53, Finance Lab, Insper Instituto de Ensino e Pesquisa.

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