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Density Prediction of Stock Index Returns Using GARCH Models: Frequentist or Bayesian Estimation?Lennart F. HoogerheideVrije Universiteit Amsterdam - Dept. of Econometrics David ArdiaLaval University - Département de Finance et Assurance; Centre interuniversitaire sur le risque, les politiques économiques et l'emploi (CIRPÉE) Nienké Corréaffiliation not provided to SSRN January 19, 2011 Economics Letters, Vol. 116, pp. 322-325, September 2012 Abstract: Using well-known GARCH models for density prediction of daily S&P 500 and Nikkei 225 index returns, a comparison is provided between frequentist and Bayesian estimation. No significant difference is found between the qualities of the forecasts of the whole density, whereas the Bayesian approach exhibits significantly better left-tail forecast accuracy.
Keywords: GARCH, Bayesian, KLIC, censored likelihood JEL Classification: C11, C22, C52 Accepted Paper SeriesDate posted: January 20, 2011 ; Last revised: May 1, 2012Suggested CitationContact Information
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