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 & Francis Journals in its journal Applied Financial Economics.
Volume (Year): 13 (2003)
Issue (Month): 8 ()
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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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- Diebold, Francis X. & Nason, James A., 1990.
"Nonparametric exchange rate prediction?,"
Journal of International Economics,
Elsevier, vol. 28(3-4), pages 315-332, May.
- Bougerol, Philippe & Picard, Nico, 1992. "Stationarity of Garch processes and of some nonnegative time series," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 115-127.
- Clements,Michael & Hendry,David, 1998.
"Forecasting Economic Time Series,"
Cambridge University Press, number 9780521634809, December.
- Tsatsura, Oleg, 2010. "A Smooth Transition GARCH-M Model," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 17(1), pages 45-61.
- Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.
- Yen-Hsien Lee & Fang Hao, 2012. "Oil and S&P 500 Markets: Evidence from the Nonlinear Model," International Journal of Economics and Financial Issues, Econjournals, vol. 2(3), pages 272-280.
- F. Javier Trivez & Beatriz Catalan, 2009. "Detecting level shifts in ARMA-GARCH (1,1) Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(6), pages 679-697.
- Deschamps, Philippe J., 2007.
"Comparing smooth transition and Markov switching autoregressive models of US Unemployment,"
DQE Working Papers
7, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 04 Jun 2008.
- Philippe J. Deschamps, 2008. "Comparing smooth transition and Markov switching autoregressive models of US unemployment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(4), pages 435-462.
- Giorgio Busetti & Matteo Manera, 2003. "STAR-GARCH Models for Stock Market Interactions in the Pacific Basin Region, Japan and US," Working Papers 2003.43, Fondazione Eni Enrico Mattei.
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