Contemporaneous-Threshold Smooth Transition GARCH Models
This paper proposes a contemporaneous-threshold smooth transition GARCH (or C-STGARCH) model for dynamic conditional heteroskedasticity. The C-STGARCH model is a generalization to second conditional moments of the contemporaneous smooth transition threshold autoregressive model of Dueker et al. (2007) in which the regime weights depend on the ex ante probability that a contemporaneous latent regime-specific variable exceeds a threshold value. A key feature of the C-STGARCH model is that its transition function depends on all the parameters of the model as well as on the data. The structural properties of the model are investigated, in addition to the finite-sample properties of the maximum likelihood estimator of its parameters. An application to U.S. stock returns illustrates the practical usefulness of the C-STGARCH model.
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Volume (Year): 15 (2011)
Issue (Month): 2 (March)
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- Michael Dueker & Martin Sola & Fabio Spagnolo, 2006.
"Contemporaneous Threshold Autoregressive Models: Estimation, Testing and Forecasting,"
Department of Economics Working Papers
2006-04, Universidad Torcuato Di Tella.
- Dueker, Michael J. & Sola, Martin & Spagnolo, Fabio, 2007. "Contemporaneous threshold autoregressive models: Estimation, testing and forecasting," Journal of Econometrics, Elsevier, vol. 141(2), pages 517-547, December.
- Michael J. Dueker & Martin Sola & Fabio Spagnolo, 2006. "Contemporaneous threshold autoregressive models: estimation, testing and forecasting," Working Papers 2003-024, Federal Reserve Bank of St. Louis.
- Michael Dueker & Martin Sola & Fabio Spagnolo, 2007. "Contemporaneous Threshold Autoregressive Models: Estimation, Testing and Forecasting," Discussion Papers 5_2007, D.E.S. (Department of Economic Studies), University of Naples "Parthenope", Italy.
- Pagan, A.R. & Schwert, G.W., 1989.
"Alternative Models For Conditional Stock Volatility,"
89-02, Rochester, Business - General.
- Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
- Adrian R. Pagan & G. William Schwert, 1989. "Alternative Models For Conditional Stock Volatility," NBER Working Papers 2955, National Bureau of Economic Research, Inc.
- LUBRANO, Michel, 1998.
"Smooth transition GARCH models: a Bayesian perspective,"
CORE Discussion Papers
1998066, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Michel LUBRANO, 2001. "Smooth Transition Garch Models : a Baysian Perspective," Discussion Papers (REL - Recherches Economiques de Louvain) 2001032, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
- Lubrano, M., 1999. "Smooth Transition GARCH Models: a Bayesian perspective," G.R.E.Q.A.M. 99a49, Universite Aix-Marseille III.
- Medeiros, Marcelo C. & Veiga, Alvaro, 2009. "Modeling Multiple Regimes In Financial Volatility With A Flexible Coefficient Garch(1,1) Model," Econometric Theory, Cambridge University Press, vol. 25(01), pages 117-161, February.
- Lanne, Markku & Saikkonen, Pentti, 2002.
"Nonlinear GARCH models for highly persistent volatility,"
SFB 373 Discussion Papers
2002,20, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- Markku Lanne & Pentti Saikkonen, 2005. "Non-linear GARCH models for highly persistent volatility," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 251-276, 07.
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