Estimating Smooth Transition Autoregressive Models with GARCH Errors in the Presence of Extreme Observations and Outliers
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.
|Date of creation:||May 2001|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.iser.osaka-u.ac.jp/index-e.html
More information through EDIRC
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.:
- 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.
- Francis X. Diebold & James M. Nason, 1989.
"Nonparametric exchange rate prediction?,"
Finance and Economics Discussion Series
81, Board of Governors of the Federal Reserve System (U.S.).
- repec:cup:cbooks:9780521634809 is not listed on IDEAS
When requesting a correction, please mention this item's handle: RePEc:dpr:wpaper:0539. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Fumiko Matsumoto)
If references are entirely missing, you can add them using this form.