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Efficient Bayesian estimation and combination of GARCH-type models

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  • Ardia, David
  • Hoogerheide, Lennart F.

Abstract

This chapter proposes an up-to-date review of estimation strategies available for the Bayesian inference of GARCH-type models. The emphasis is put on a novel efficient procedure named AdMitIS. The methodology automatically constructs a mixture of Student-t distributions as an approximation to the posterior density of the model parameters. This density is then used in importance sampling for model estimation, model selection and model combination. The procedure is fully automatic which avoids difficult and time consuming tuning of MCMC strategies. The AdMitIS methodology is illustrated with an empirical application to S&P index log-returns where non-nested GARCH-type models are estimated and combined to predict the distribution of next-day ahead log-returns.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 22919.

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Date of creation: 08 Feb 2010
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Handle: RePEc:pra:mprapa:22919

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Keywords: GARCH; Bayesian inference; MCMC; marginal likelihood; Bayesian model averaging; adaptive mixture of Student-t distributions; importance sampling.;

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References

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Citations

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Cited by:
  1. Ardia, David & Lennart, Hoogerheide & Nienke, Corré, 2011. "Stock index returns’ density prediction using GARCH models: Frequentist or Bayesian estimation?," MPRA Paper 28259, University Library of Munich, Germany.
  2. Hoogerheide, Lennart F. & Ardia, David & Corré, Nienke, 2012. "Density prediction of stock index returns using GARCH models: Frequentist or Bayesian estimation?," Economics Letters, Elsevier, vol. 116(3), pages 322-325.
  3. Audrone Virbickaite & Concepción Ausín & Pedro Galeano, 2013. "A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection," Statistics and Econometrics Working Papers ws131009, Universidad Carlos III, Departamento de Estadística y Econometría.

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