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

Author

Listed:
  • Lennart F. Hoogerheide

    (Erasmus University Rotterdam)

  • David Ardia

    (aeris CAPITAL)

  • Nienke Corre

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.

Suggested Citation

  • Lennart F. Hoogerheide & David Ardia & Nienke Corre, 2011. "Stock Index Returns' Density Prediction using GARCH Models: Frequentist or Bayesian Estimation?," Tinbergen Institute Discussion Papers 11-020/4, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20110020
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    References listed on IDEAS

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    More about this item

    Keywords

    GARCH; Bayesian; KLIC; censored likelihood;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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