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

  • Felix Chan
  • Michael McAleer

The 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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File URL: http://www.tandfonline.com/doi/abs/10.1080/0960310022000029295
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Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

Volume (Year): 13 (2003)
Issue (Month): 8 ()
Pages: 581-592

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Handle: RePEc:taf:apfiec:v:13:y:2003:i:8:p:581-592
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  1. Francis X. Diebold & James M. Nason, 1989. "Nonparametric exchange rate prediction?," Finance and Economics Discussion Series 81, Board of Governors of the Federal Reserve System (U.S.).
  2. repec:cup:cbooks:9780521634809 is not listed on IDEAS
  3. 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.
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