Parameter estimation in nonlinear AR-GARCH models

This file is part of IDEAS, which uses RePEc data

Parameter estimation in nonlinear AR-GARCH models

Author Info

Mika Meitz
Pentti Saikkonen

Additional information is available for the following registered author(s):

Mika Meitz

Abstract

This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a functional coefficient autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first order generalized autoregressive conditional heteroskedasticity (GARCH (1,1)) model. Strong consistency and asymptotic normality of the global Gaussian quasi maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

Publisher Info
Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 396.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 2008
Date of revision:
Handle: RePEc:oxf:wpaper:396

Contact details of provider:
Postal: Manor Rd. Building, Oxford, OX1 3UQ
Email:
Web page: http://www.economics.ox.ac.uk/
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Mark George).

Other versions of this item:

Paper
- Mika Meitz & Pentti Saikkonen, 2008. "Parameter estimation in nonlinear AR-GARCH models," CREATES Research Papers 2008-30, School of Economics and Management, University of Aarhus. [Downloadable!]
- Mika Meitz & Pentti Saikkonen, 2008. "Parameter Estimation in Nonlinear AR-GARCH Models," Economics Working Papers ECO2008/25, European University Institute. [Downloadable!]

Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

This paper has been announced in the following NEP Reports:

NEP-ALL-2008-06-13 (All new papers)
NEP-ETS-2008-06-13 (Econometric Time Series)
NEP-ORE-2008-06-13 (Operations Research)

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

Meitz, Mika & Saikkonen, Pentti, 2008. "Ergodicity, Mixing, And Existence Of Moments Of A Class Of Markov Models With Applications To Garch And Acd Models," Econometric Theory, Cambridge University Press, vol. 24(05), pages 1291-1320, October. [Downloadable!]
Other versions:
- Meitz, Mika & Saikkonen, Pentti, 2004. "Ergodicity, mixing, and existence of moments of a class of Markov models with applications to GARCH and ACD models," Working Paper Series in Economics and Finance 573, Stockholm School of Economics, revised 20 Apr 2007. [Downloadable!]
- Mika Meitz & Pentti Saikkonen, 2007. "Ergodicity, mixing, and existence of moments of a class of Markov models with applications to GARCH and ACD models," Economics Series Working Papers 327, University of Oxford, Department of Economics. [Downloadable!]
Dick van Dijk & Timo Teräsvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models - A Survey Of Recent Developments," Econometric Reviews, Taylor and Francis Journals, vol. 21(1), pages 1-47. [Downloadable!] (restricted)
Other versions:
- van Dijk, Dick & Teräsvirta, Timo & Franses, Philip Hans, 2000. "Smooth Transition Autoregressive Models - A Survey of Recent Developments," Working Paper Series in Economics and Finance 380, Stockholm School of Economics, revised 17 Jan 2001. [Downloadable!]
- D. van Dijk & T. Terasvirta & P.H. Franses, 2000. "Smooth transition autoregressive models - A survey of recent developments," Econometric Institute Report 200, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
- Dijk, D.J.C. van & Terasvirta, T. & Franses, Ph.H.B.F., 2000. "Smooth transition autoregressive models - A survey of recent developments," Econometric Institute Report EI 2000-23/A Revision_Dat, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(02), pages 280-310, April. [Downloadable!]
Other versions:
- Shiqing Ling & Michael McAleer, 2001. "Asymptotic Theory for a Vector ARMA-GARCH Model," ISER Discussion Paper 0549, Institute of Social and Economic Research, Osaka University. [Downloadable!]
Mika Meitz & Pentti Saikkonen, 2008. "Stability of nonlinear AR-GARCH models," Journal of Time Series Analysis, Blackwell Publishing, vol. 29(3), pages 453-475, 05. [Downloadable!] (restricted)
Other versions:
- Mika Meitz & Pentti Saikkonen, 2007. "Stability of nonlinear AR-GARCH models," Economics Series Working Papers 328, University of Oxford, Department of Economics. [Downloadable!]
- MEITZ, Mika & SAIKKONEN, Pentti, 2006. "Stability of nonlinear AR-GARCH models," CORE Discussion Papers 2006078, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
- Meitz, Mika & Saikkonen, Pentti, 2006. "Stability of nonlinear AR-GARCH models," Working Paper Series in Economics and Finance 632, Stockholm School of Economics. [Downloadable!]
Ling, Shiqing, 2007. "Self-weighted and local quasi-maximum likelihood estimators for ARMA-GARCH/IGARCH models," Journal of Econometrics, Elsevier, vol. 140(2), pages 849-873, October. [Downloadable!] (restricted)
Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June. [Downloadable!] (restricted)
Other versions:
- Zhuanxin Ding & Clive Granger & Robert Engle, 1992. "A Long Memory Property of Stock Market Returns and a New Model," University of California at San Diego, Economics Working Paper Series 92-21, Department of Economics, UC San Diego.
Donald W. K. Andrews, 1999. "Estimation When a Parameter Is on a Boundary," Econometrica, Econometric Society, vol. 67(6), pages 1341-1384, November.
Lundbergh, Stefan & Terasvirta, Timo, 2002. "Evaluating GARCH models," Journal of Econometrics, Elsevier, vol. 110(2), pages 417-435, October. [Downloadable!] (restricted)
Other versions:
- Stefan Lundbergh & Timo Teräsvirta, 1999. "Evaluating GARCH Models," Tinbergen Institute Discussion Papers 99-008/4, Tinbergen Institute. [Downloadable!]
- Lundbergh, Stefan & Teräsvirta, Timo, 1998. "Evaluating GARCH models," Working Paper Series in Economics and Finance 292, Stockholm School of Economics, revised 03 May 1999.
J. Pfanzagl, 1969. "On the measurability and consistency of minimum contrast estimates," Metrika, Springer, vol. 14(1), pages 249-272, December. [Downloadable!] (restricted)
Kristensen, Dennis & Rahbek, Anders, 2005. "ASYMPTOTICS OF THE QMLE FOR A CLASS OF ARCH(q) MODELS," Econometric Theory, Cambridge University Press, vol. 21(05), pages 946-961, October. [Downloadable!]
Jensen, S ren Tolver & Rahbek, Anders, 2004. "Asymptotic Inference For Nonstationary Garch," Econometric Theory, Cambridge University Press, vol. 20(06), pages 1203-1226, December. [Downloadable!]
Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November. [Downloadable!] (restricted)
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," Staff Report 157, Federal Reserve Bank of Minneapolis. [Downloadable!]
Other versions:
- 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. [Downloadable!] (restricted)
Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April. [Downloadable!] (restricted)
M. Lanne & P. Saikkonen, . "Nonlinear GARCH Models for Highly Persistent Volatility," Sonderforschungsbereich 373 2002-20, Humboldt Universitaet Berlin.
Other versions:
- 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. [Downloadable!] (restricted)
Francq, Christian & Zako an, Jean-Michel, 2006. "Mixing Properties Of A General Class Of Garch(1,1) Models Without Moment Assumptions On The Observed Process," Econometric Theory, Cambridge University Press, vol. 22(05), pages 815-834, October. [Downloadable!]
Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July. [Downloadable!] (restricted)

Full references

Statistics
Access and download statistics

Did you know? About 1000 archives contribute their bibliographic data to RePEc.

This page was last updated on 2009-11-18.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.