Estimating smooth transition autoregressive models with GARCH errors in the presence of extreme observations and outliers
AbstractThe paper investigates several empirical issues regarding quasi-maximum likelihood estimation of smooth transition autoregressive (STAR) models with GARCH errors (STAR-GARCH) and STAR models with smooth transition GARCH errors (STAR-STGARCH). Empirical evidence is provided to show that different algorithms produce substantially different estimates for the same model. Consequently, the interpretation of the model can differ according to the choice of algorithm. Convergence, the choice of different algorithms for maximizing the likelihood function, and the sensitivity of the estimates to outliers and extreme observations, are examined using daily data for S&P 500, Hang Seng and Nikkei 225 for the period January 1986 to April 2000.
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Bibliographic InfoArticle provided by Taylor and Francis Journals in its journal Applied Financial Economics.
Volume (Year): 13 (2003)
Issue (Month): 8 ()
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Web page: http://www.tandf.co.uk/journals/routledge/09603107.html
Other versions of this item:
- Felix Chan & Michael McAleer, 2001. "Estimating Smooth Transition Autoregressive Models with GARCH Errors in the Presence of Extreme Observations and Outliers," ISER Discussion Paper 0539, Institute of Social and Economic Research, Osaka University.
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