Density Prediction of Stock Index Returns Using GARCH Models: Frequentist or Bayesian Estimation?
Lennart F. Hoogerheide
Vrije Universiteit Amsterdam - Dept. of Econometrics
Laval University - Département de Finance et Assurance; Centre interuniversitaire sur le risque, les politiques économiques et l'emploi (CIRPÉE)
affiliation not provided to SSRN
January 19, 2011
Economics Letters, Vol. 116, pp. 322-325, September 2012
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, C52Accepted Paper Series
Date posted: January 20, 2011 ; Last revised: May 1, 2012
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