A Smooth Transition GARCH-M Model
AbstractGeneralized autoregressive conditional heteroscedasticity in-mean model allows accounting for both time-varying variance and risk premium in financial time series data. This paper introduces an extension of this particular model with more flexible parameterization of the way variance enters the conditional mean equation, which allows for more complex dynamics in the time-varying risk premium. Paper presents model specification, criteria for hypothesis testing and develops an application for several stock exchange indexes. Results suggest evidence that proposed model may be more preferable to standard GARCH-in-mean model.
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Bibliographic InfoArticle provided by Publishing House "SINERGIA PRESS" in its journal Applied Econometrics.
Volume (Year): 17 (2010)
Issue (Month): 1 ()
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Nonlinear GARCH; volatility; risk premium; varying parameters;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
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- Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, School of Economics and Management, University of Aarhus.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
- 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.
- Felix Chan & Michael McAleer, 2003. "Estimating smooth transition autoregressive models with GARCH errors in the presence of extreme observations and outliers," Applied Financial Economics, Taylor & Francis Journals, vol. 13(8), pages 581-592.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993.
"On the relation between the expected value and the volatility of the nominal excess return on stocks,"
157, Federal Reserve Bank of Minneapolis.
- Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
- Fiorentini, Gabriele & Calzolari, Giorgio & Panattoni, Lorenzo, 1996.
"Analytic Derivatives and the Computation of GARCH Estimates,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 11(4), pages 399-417, July-Aug..
- Fiorentini,G. & Calzolari,G. & Panattoni,L., 1995. "Analytic Derivatives and the Computation of Garch Estimates," Papers 9519, Centro de Estudios Monetarios Y Financieros-.
- Lundbergh, Stefan & Terasvirta, Timo, 2002.
"Evaluating GARCH models,"
Journal of Econometrics,
Elsevier, vol. 110(2), pages 417-435, October.
- Stefan Lundbergh & Timo Teräsvirta, 1999. "Evaluating GARCH Models," Tinbergen Institute Discussion Papers 99-008/4, Tinbergen Institute.
- Lundbergh, Stefan & Teräsvirta, Timo, 1998. "Evaluating GARCH models," Working Paper Series in Economics and Finance 292, Stockholm School of Economics, revised 03 May 1999.
- Menelaos Karanasos & J. Kim, . "Alternative GARCH in Mean Models: An Application to the Korean Stock Market," Discussion Papers 00/25, Department of Economics, University of York.
- Godfrey,L. G., 1991. "Misspecification Tests in Econometrics," Cambridge Books, Cambridge University Press, number 9780521424592.
- Annastiina Silvennoinen & Timo Teräsvirta, 2008.
"Multivariate GARCH models,"
CREATES Research Papers
2008-06, School of Economics and Management, University of Aarhus.
- Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
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