Estimating smooth transition autoregressive models with GARCH errors in the presence of extreme observations and outliers

Felix Chan and Michael McAleer

This paper investigates several empirical issues regarding quasimaximum likelihood estimation of Smooth Transition Autoregressive (STAR) models with GARCH errors, specifically STAR-GARCH and STAR-STGARCH. Convergence, the choice of different algorithms for maximising the likelihood function, and the sensitivity of the estimates to outliers and extreme observations, are examined using daily data for S&P 500, Heng Seng and Nikkei 225 for the period January 1986 to April 2000.

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Year of publication:	May 2001 ; [Elektronische Ressource]
Other Persons:	Chan, Felix (contributor) ; McAleer, Michael (contributor)
Institutions:	Shakai-Keizai-Kenkyūsho <Osaka> (contributor)
Publisher:	Osaka : Inst. of Social and Economic Research
Subject:	ARCH-Modell \| ARCH model \| Theorie \| Theory \| Maximum-Likelihood-Schätzung \| Maximum likelihood estimation

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Extent:	Online Ressource, 32 p., text
Series:	Discussion paper / Institute of Social and Economic Research. - Osaka : [Verlag nicht ermittelbar], ISSN 2435-0982, ZDB-ID 2106942-6. - Vol. 539
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Arbeitspapier ; Working Paper ; Graue Literatur ; Non-commercial literature
Language:	English
Notes:	Systemvoraussetzungen: Acrobat reader
Other identifiers:	hdl:10419/92706 [Handle]
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10001644307