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Estimating Smooth Transition Autoregressive Models with GARCH Errors in the Presence of Extreme Observations and Outliers

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Author Info
Felix Chan
Michael McAleer

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Abstract

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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Paper provided by Institute of Social and Economic Research, Osaka University in its series ISER Discussion Paper with number 0539.

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Date of creation: May 2001
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Handle: RePEc:dpr:wpaper:0539

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  1. Felix Chan & Dora Marinova & Michael McAleer, 2003. "Modelling the Asymmetric Volatility of Electronics Patents in the USA," CIRJE F-Series CIRJE-F-208, CIRJE, Faculty of Economics, University of Tokyo. [Downloadable!]
  2. 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. [Downloadable!]
  3. 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. [Downloadable!]
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