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

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  • Felix Chan
  • Michael McAleer

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.

Suggested Citation

  • 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.
    • Handle: RePEc:dpr:wpaper:0539
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    References listed on IDEAS

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    1. Lundbergh, Stefan & Teräsvirta, Timo, 2000. "Forecasting with smooth transition autoregressive models," SSE/EFI Working Paper Series in Economics and Finance 390, Stockholm School of Economics.
    2. Stefan Lundbergh & Timo Teräsvirta, 1999. "Modelling Economic High-Frequency Time Series," Tinbergen Institute Discussion Papers 99-009/4, Tinbergen Institute.
    3. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
    4. 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.
    5. Franses, Ph.H.B.F. & Neele, J. & van Dijk, D.J.C., 1998. "Forecasting volatility with switching persistence GARCH models," Econometric Institute Research Papers EI 9819, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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