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

Ardia, David; Lennart, Hoogerheide and Nienke, Corré (2011): Stock index returns’ density prediction using GARCH models: Frequentist or Bayesian estimation? Unpublished.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: GARCH; Bayesian; KLIC; censored likelihood
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation and Selection; C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C22 - Time-Series Models; Dynamic Quantile Regressions ; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C11 - Bayesian Analysis

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