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

6 Pages Posted: 20 Jan 2011 Last revised: 17 Nov 2017

See all articles by Lennart F. Hoogerheide

Lennart F. Hoogerheide

VU University Amsterdam

David Ardia

HEC Montreal - Department of Decision Sciences

Nienké Corré

Bain & Company - Bain & Company, Netherlands, LLC

Date Written: January 19, 2011

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

JEL Classification: C11, C22, C52

Suggested Citation

Hoogerheide, Lennart F. and Ardia, David and Corré, Nienké, Stock Index Returns' Density Prediction Using GARCH Models: Frequentist or Bayesian Estimation? (January 19, 2011). Economics Letters, Vol. 116, pp. 322-325, September 2012, Available at SSRN: https://ssrn.com/abstract=1743703 or http://dx.doi.org/10.2139/ssrn.1743703

Lennart F. Hoogerheide (Contact Author)

VU University Amsterdam ( email )

De Boelelaan 1105
Amsterdam, ND North Holland 1081 HV
Netherlands

David Ardia

HEC Montreal - Department of Decision Sciences ( email )

3000 Côte-Sainte-Catherine Road
Montreal, QC H2S1L4
Canada

Nienké Corré

Bain & Company - Bain & Company, Netherlands, LLC ( email )

Amstelplein 1
Amsterdam
Netherlands

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