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

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

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Year of publication:	2017
Authors:	Hoogerheide, Lennart F.
Other Persons:	Ardia, David (contributor) ; Corré, Nienké (contributor)
Publisher:	[2017]: [S.l.] : SSRN
Subject:	ARCH-Modell \| ARCH model \| Prognoseverfahren \| Forecasting model \| Theorie \| Theory \| Kapitaleinkommen \| Capital income \| Bayes-Statistik \| Bayesian inference \| Modellierung \| Scientific modelling \| Aktienindex \| Stock index

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Extent:	1 Online-Ressource (6 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	In: Economics Letters, Vol. 116, pp. 322-325, September 2012 Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 19, 2011 erstellt
Other identifiers:	10.2139/ssrn.1743703 [DOI]
Classification:	C11 - Bayesian Analysis ; C22 - Time-Series Models ; C52 - Model Evaluation and Testing
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10012976219