Stock index returns’ density prediction using GARCH models: Frequentist or Bayesian estimation?

David Ardia, Hoogerheide Lennart and Corré Nienke

MPRA Paper from University Library of Munich, Germany

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 (search for similar items in EconPapers)
JEL-codes: C11 C22 C52 (search for similar items in EconPapers)
Date: 2011-01-17
New Economics Papers: this item is included in nep-ets and nep-for
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