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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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    Cited by:

    1. 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.
    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.
    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.
    4. Tsatsura, Oleg, 2010. "A Smooth Transition GARCH-M Model," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 17(1), pages 45-61.
    5. Melike Bildirici & Özgür Ömer Ersin, 2014. "Nonlinearity, Volatility and Fractional Integration in Daily Oil Prices: Smooth Transition Autoregressive ST-FI(AP)GARCH Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 108-135, October.
    6. Christopher Krauss & Klaus Herrmann, 2017. "On the Power and Size Properties of Cointegration Tests in the Light of High-Frequency Stylized Facts," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 10(1), pages 1-24, February.
    7. Murat Midilic, 2016. "Estimation Of Star-Garch Models With Iteratively Weighted Least Squares," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 16/918, Ghent University, Faculty of Economics and Business Administration.
    8. 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.
    9. Chan, Felix & Marinova, Dora & McAleer, Michael, 2004. "Modelling the asymmetric volatility of electronics patents in the USA," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 169-184.
    10. Chan, Felix & Theoharakis, Billy, 2011. "Estimating m-regimes STAR-GARCH model using QMLE with parameter transformation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1385-1396.
    11. Petri Maki-Franti, 2008. "Money and stock returns: is there habit formation for holding liquid assets?," International Economic Journal, Taylor & Francis Journals, vol. 22(1), pages 63-80.
    12. Hubner, Stefan, 2016. "Topics in nonparametric identification and estimation," Other publications TiSEM 08fce56b-3193-46e0-871b-0, Tilburg University, School of Economics and Management.
    13. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    14. 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.

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