Home > Research > Publications & Outputs > R-estimators in GARCH models

Electronic data

  • Binder1

    Accepted author manuscript, 875 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License


Text available via DOI:

View graph of relations

R-estimators in GARCH models: asymptotics and applications

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>31/12/2021
<mark>Journal</mark>The Econometrics Journal
Issue number1
Number of pages16
Pages (from-to)98-113
Publication StatusPublished
Early online date30/08/21
<mark>Original language</mark>English


The quasi-maximum likelihood estimation is a commonly-used method for estimating the GARCH parameters. However, such estimators are sensitive to outliers and their asymptotic normality is proved under the finite fourth moment assumption on the underlying error distribution. In this paper, we propose a novel class of estimators of the GARCH parameters based on ranks of the residuals, called R-estimators, with the property that they are asymptotically normal under the existence of a finite $2+\delta$ moment of the errors and are highly efficient.
We propose fast algorithm for computing the R-estimators.
Both real data analysis and simulations show the superior performance of the
proposed estimators under the heavy-tailed and asymmetric distributions.