Weighted trimmed likelihood estimator for GARCH models
Chalabi, Yohan / Y. and
Diethelm Wuertz
MPRA Paper from University Library of Munich, Germany
Abstract:
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.
Keywords: GARCH Models; Robust Estimators; Outliers; Weighted Trimmed Likelihood Estimator (WTLE); Quasi Maximum Likelihood Estimator (QMLE) (search for similar items in EconPapers)
JEL-codes: C40 (search for similar items in EconPapers)
Date: 2010-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
References: Add references at CitEc
Citations:
Downloads: (external link)
https://mpra.ub.uni-muenchen.de/26536/1/MPRA_paper_26536.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/42903/1/MPRA_paper_42903.pdf revised version (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:26536
Access Statistics for this paper
More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter (winter@lmu.de).