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

Lennart F. Hoogerheide (), David Ardia and Nienke Corre
Additional contact information
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
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://papers.tinbergen.nl/11020.pdf (application/pdf)

Related works:
Working Paper: Stock index returns’ density prediction using GARCH models: Frequentist or Bayesian estimation? (2011)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20110020

Access Statistics for this paper

More papers in Tinbergen Institute Discussion Papers from Tinbergen Institute Contact information at EDIRC.
Bibliographic data for series maintained by Tinbergen Office +31 (0)10-4088900 ().