Weighted trimmed likelihood estimator for GARCH models
Generalized autoregressive heteroskedasticity (GARCH) models are widely used to reproduce stylized facts of ﬁnancial time series and today play an essential role in risk management and volatility forecasting. But despite extensive research, problems are still encountered during parameter estimation in the presence of outliers. Here we show how this limitation can be overcome by applying the robust weighted trimmed likelihood estimator (WTLE) to the standard GARCH model. We suggest a fast implementation and explain how the additional robust parameter can be automatically estimated. We compare our approach with other recently introduced robust GARCH estimators and show through the results of an extensive simulation study that the proposed estimator provides robust and reliable estimates with a small computation cost. Moreover, the proposed fully automatic method for selecting the trimming parameter obviates the tedious ﬁne tuning process required by other models to obtain a “robust” parameter, which may be appreciated by practitioners.
|Date of creation:||Oct 2010|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
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.:
- Charles, Amelie & Darne, Olivier, 2005. "Outliers and GARCH models in financial data," Economics Letters, Elsevier, vol. 86(3), pages 347-352, March.
- Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
- Čížek, Pavel, 2008.
"General Trimmed Estimation: Robust Approach To Nonlinear And Limited Dependent Variable Models,"
Cambridge University Press, vol. 24(06), pages 1500-1529, December.
- Cizek, P., 2004. "General Trimmed Estimation : Robust Approach to Nonlinear and Limited Dependent Variable Models," Discussion Paper 2004-130, Tilburg University, Center for Economic Research.
- Neykov, N. & Filzmoser, P. & Dimova, R. & Neytchev, P., 2007. "Robust fitting of mixtures using the trimmed likelihood estimator," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 299-308, September.
- 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.
- Brooks, Chris & Burke, Simon P. & Persand, Gita, 2001. "Benchmarks and the accuracy of GARCH model estimation," International Journal of Forecasting, Elsevier, vol. 17(1), pages 45-56.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:26536. 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: (Joachim Winter)
If references are entirely missing, you can add them using this form.