This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Asymptotically Efficient Estimation of the Change Point for Semiparametric GARCH models

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Takayuki Shiohama
Abstract

Instability of volatility parameters in GARCH models in an important issue for analyzing financial time series. In this paper we investigate the asymptotic theory for change point estimators in semiparametric GARCH models. When the parameters of a GARCH models have changed within an observed realization, two types estimators, Maximum likelihood estimator (MLE) and Bayesian estimator (BE), are proposed. Then we derive the asymptotic distributions for these estimators. The MLE and BE have different limit laws, and the BE is asymptotically efficient. Monte Carlo studies on the finite sample behaviors are conducted.

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.

File URL: http://www.ier.hit-u.ac.jp/Common/publication/DP/DP471.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by Institute of Economic Research, Hitotsubashi University in its series Discussion Paper Series with number a471.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: Jan 2006
Date of revision:
Handle: RePEc:hit:hituec:a471

Contact details of provider:
Postal: 2-1 Naka, Kunitachi City, Tokyo 186
Phone: +81-42-580-8327
Fax: +81-42-580-8333
Email:
Web page: http://www.ier.hit-u.ac.jp/
More information through EDIRC

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

Related research
Keywords: GARCH process; change point; maximum likelihood estimator; Bayesian estimator; asymptotic efficiency;

Other versions of this item:

This paper has been announced in the following NEP Reports: 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.:
  1. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290. [Downloadable!] (restricted)
    Other versions:
  2. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-34, April.
  3. Thomas Mikosch & Catalin Starica, 2004. "Long range dependence effects and ARCH modelling," Econometrics 0412004, EconWPA. [Downloadable!]
  4. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59. [Downloadable!] (restricted)
  5. Lundbergh, Stefan & Terasvirta, Timo, 2002. "Evaluating GARCH models," Journal of Econometrics, Elsevier, vol. 110(2), pages 417-435, October. [Downloadable!] (restricted)
    Other versions:
  6. 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. [Downloadable!] (restricted)
  7. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333. [Downloadable!] (restricted)
    Other versions:
  8. Berkes, Istvan & Horváth, Lajos & Kokoszka, Piotr, 2004. "Testing for parameter constancy in GARCH(p,q) models," Statistics & Probability Letters, Elsevier, vol. 70(4), pages 263-273, December. [Downloadable!] (restricted)
  9. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22. [Downloadable!]
    Other versions:
  10. Francis Dieobold, 1986. "Modeling The persistence Of Conditional Variances: A Comment," Econometric Reviews, Taylor and Francis Journals, vol. 5(1), pages 51-56. [Downloadable!] (restricted)
  11. Shiqing Ling & Michael McAleer, 2001. "On Adaptive Estimation in Nonstationary ARMA Models with GARCH Errors," ISER Discussion Paper 0548, Institute of Social and Economic Research, Osaka University. [Downloadable!]
  12. Heston, Steven L & Nandi, Saikat, 2000. "A Closed-Form GARCH Option Valuation Model," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 13(3), pages 585-625.
  13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April. [Downloadable!] (restricted)
  14. Drost, Feike C. & Klaassen, Chris A. J., 1997. "Efficient estimation in semiparametric GARCH models," Journal of Econometrics, Elsevier, vol. 81(1), pages 193-221, November. [Downloadable!] (restricted)
    Other versions:
Full references

Statistics
Access and download statistics

Did you know? There are over 21000 authors registered on RePEc Author Service.

This page was last updated on 2009-11-25.


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.