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Stock Index Returns' Density Prediction using GARCH Models: Frequentist or Bayesian Estimation?

Lennart F. Hoogerheide (), David Ardia and Nienke Corre
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Lennart F. Hoogerheide: Erasmus University Rotterdam

No 11-020/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This discussion paper resulted in an article in Economics Letters , 2012, 116(3), 322-325. 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 C52 C53 C58 (search for similar items in EconPapers)
Date: 2011-01-31
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Working Paper: Stock index returns’ density prediction using GARCH models: Frequentist or Bayesian estimation? (2011) Downloads
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