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Article: Bootstrap Inference for Garch Models by the Least Absolute Deviation Estimation

TitleBootstrap Inference for Garch Models by the Least Absolute Deviation Estimation
Authors
KeywordsBootstrap method
exchangeable weights
GARCH models
generalized bootstrap
LAD estimator
Issue Date2020
PublisherWiley. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9892
Citation
Journal of Time Series Analysis, 2020, v. 41 n. 1, p. 21-40 How to Cite?
AbstractThis article considers the generalized bootstrap method to approximate the least absolute deviation estimation and portmanteau test for generalized autoregressive conditional heteroskedastic models. The generalized bootstrap approach is easy‐to‐implement, and includes many bootstrap methods as special cases, such as Efron's bootstrap, Bayesian bootstrap, and random‐weighting bootstrap. The proposed bootstrap procedure is shown to be asymptotically valid for both estimation and test. The finite‐sample performance is assessed by simulation studies, and its usefulness is illustrated by a real application to the Hang Seng Index.
Persistent Identifierhttp://hdl.handle.net/10722/286689
ISSN
2019 Impact Factor: 0.817
2015 SCImago Journal Rankings: 1.177

 

DC FieldValueLanguage
dc.contributor.authorZhu, Q-
dc.contributor.authorZeng, R-
dc.contributor.authorLi, G-
dc.date.accessioned2020-09-04T13:29:02Z-
dc.date.available2020-09-04T13:29:02Z-
dc.date.issued2020-
dc.identifier.citationJournal of Time Series Analysis, 2020, v. 41 n. 1, p. 21-40-
dc.identifier.issn0143-9782-
dc.identifier.urihttp://hdl.handle.net/10722/286689-
dc.description.abstractThis article considers the generalized bootstrap method to approximate the least absolute deviation estimation and portmanteau test for generalized autoregressive conditional heteroskedastic models. The generalized bootstrap approach is easy‐to‐implement, and includes many bootstrap methods as special cases, such as Efron's bootstrap, Bayesian bootstrap, and random‐weighting bootstrap. The proposed bootstrap procedure is shown to be asymptotically valid for both estimation and test. The finite‐sample performance is assessed by simulation studies, and its usefulness is illustrated by a real application to the Hang Seng Index.-
dc.languageeng-
dc.publisherWiley. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9892-
dc.relation.ispartofJournal of Time Series Analysis-
dc.rightsPreprint This is the pre-peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Postprint This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.subjectBootstrap method-
dc.subjectexchangeable weights-
dc.subjectGARCH models-
dc.subjectgeneralized bootstrap-
dc.subjectLAD estimator-
dc.titleBootstrap Inference for Garch Models by the Least Absolute Deviation Estimation-
dc.typeArticle-
dc.identifier.emailZhu, Q: zhu.qianqian@mail.shufe.edu.cn-
dc.identifier.emailLi, G: gdli@hku.hk-
dc.identifier.authorityLi, G=rp00738-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/jtsa.12474-
dc.identifier.scopuseid_2-s2.0-85065811259-
dc.identifier.hkuros313954-
dc.identifier.volume41-
dc.identifier.issue1-
dc.identifier.spage21-
dc.identifier.epage40-
dc.publisher.placeUnited Kingdom-

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