Bootstrapping M-estimators in GARCH models

Mukherjee, Kanchan (2020) Bootstrapping M-estimators in GARCH models. Biometrika, 107 (3). pp. 753-760. ISSN 0006-3444

[img]
Text (Main paper)
Main_paper.pdf - Accepted Version
Restricted to Repository staff only until 6 May 2021.
Available under License Creative Commons Attribution-NonCommercial.

Download (196kB)

Abstract

We consider the weighted bootstrap approximation of the distribution of a class of M-estimators of the GARCH (p, q) parameters. We prove that the bootstrap distribution, given the data, is a consistent estimate in probability of the distribution of the M-estimator which is asymptotically normal. We propose an algorithm for the computation of M-estimates which at the same time is software-friendly to compute the bootstrap replicates from the given data. Our simulation study indicates superior coverage rates for various weighted bootstrap schemes compared with the rates based on the normal approximation and the existing bootstrap methods in the literature such as percentile t-subsampling schemes for the GARCH model. Since some familiar bootstrap schemes are special cases of the weighted bootstrap, this paper thus provides a unified theory and algorithm for bootstrapping in GARCH models.

Item Type:
Journal Article
Journal or Publication Title:
Biometrika
Additional Information:
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version K Mukherjee, Bootstrapping M-estimators in generalized autoregressive conditional heteroscedastic models, Biometrika, Volume 107, Issue 3, September 2020, Pages 753–760, is available online at: https://academic.oup.com/biomet/article-abstract/107/3/753/5831314
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1100/1101
Subjects:
ID Code:
138306
Deposited By:
Deposited On:
04 Nov 2019 14:55
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Sep 2020 09:35