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Article: A New Pearson-Type QMLE for Conditionally Heteroscedastic Models
Title | A New Pearson-Type QMLE for Conditionally Heteroscedastic Models |
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Authors | |
Keywords | Asymmetric innovation Conditionally heteroscedastic model Exchange rates GARCH model Leptokurtic innovation Non-Gaussian QMLE Pearsonian QMLE Pearson’s Type IV distribution Stock indexes |
Issue Date | 2015 |
Publisher | Taylor & Francis Inc. The Journal's web site is located at http://www.tandfonline.com/loi/ubes20 |
Citation | Journal of Business and Economic Statistics, 2015, v. 33 n. 4, p. 552-565 How to Cite? |
Abstract | This article proposes a novel Pearson-type quasi-maximum likelihood estimator (QMLE) of GARCH(p, q) models. Unlike the existing Gaussian QMLE, Laplacian QMLE, generalized non-Gaussian QMLE, or LAD estimator, our Pearsonian QMLE (PQMLE) captures not just the heavy-tailed but also the skewed innovations. Under strict stationarity and some weak moment conditions, the strong consistency and asymptotic normality of the PQMLE are obtained. With no further efforts, the PQMLE can be applied to other conditionally heteroscedastic models. A simulation study is carried out to assess the performance of the PQMLE. Two applications to four major stock indexes and two exchange rates further highlight the importance of our new method. Heavy-tailed and skewed innovations are often observed together in practice, and the PQMLE now gives us a systematic way to capture these two coexisting features. © 2015 American Statistical Association. |
Persistent Identifier | http://hdl.handle.net/10722/222907 |
ISSN | 2023 Impact Factor: 2.9 2023 SCImago Journal Rankings: 3.385 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhu, K | - |
dc.contributor.author | Li, WK | - |
dc.date.accessioned | 2016-02-12T06:35:32Z | - |
dc.date.available | 2016-02-12T06:35:32Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Journal of Business and Economic Statistics, 2015, v. 33 n. 4, p. 552-565 | - |
dc.identifier.issn | 0735-0015 | - |
dc.identifier.uri | http://hdl.handle.net/10722/222907 | - |
dc.description.abstract | This article proposes a novel Pearson-type quasi-maximum likelihood estimator (QMLE) of GARCH(p, q) models. Unlike the existing Gaussian QMLE, Laplacian QMLE, generalized non-Gaussian QMLE, or LAD estimator, our Pearsonian QMLE (PQMLE) captures not just the heavy-tailed but also the skewed innovations. Under strict stationarity and some weak moment conditions, the strong consistency and asymptotic normality of the PQMLE are obtained. With no further efforts, the PQMLE can be applied to other conditionally heteroscedastic models. A simulation study is carried out to assess the performance of the PQMLE. Two applications to four major stock indexes and two exchange rates further highlight the importance of our new method. Heavy-tailed and skewed innovations are often observed together in practice, and the PQMLE now gives us a systematic way to capture these two coexisting features. © 2015 American Statistical Association. | - |
dc.language | eng | - |
dc.publisher | Taylor & Francis Inc. The Journal's web site is located at http://www.tandfonline.com/loi/ubes20 | - |
dc.relation.ispartof | Journal of Business and Economic Statistics | - |
dc.rights | This is an electronic version of an article published in Journal of Business and Economic Statistics, 2015, v. 33 n. 4, p. 552-565. The article is available online at: http://dx.doi.org/10.1080/07350015.2014.977446 | - |
dc.subject | Asymmetric innovation | - |
dc.subject | Conditionally heteroscedastic model | - |
dc.subject | Exchange rates | - |
dc.subject | GARCH model | - |
dc.subject | Leptokurtic innovation | - |
dc.subject | Non-Gaussian QMLE | - |
dc.subject | Pearsonian QMLE | - |
dc.subject | Pearson’s Type IV distribution | - |
dc.subject | Stock indexes | - |
dc.title | A New Pearson-Type QMLE for Conditionally Heteroscedastic Models | - |
dc.type | Article | - |
dc.identifier.email | Zhu, K: mazhuke@hku.hk | - |
dc.identifier.email | Li, WK: hrntlwk@hkucc.hku.hk | - |
dc.identifier.authority | Zhu, K=rp02199 | - |
dc.identifier.authority | Li, WK=rp00741 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1080/07350015.2014.977446 | - |
dc.identifier.scopus | eid_2-s2.0-84945269825 | - |
dc.identifier.hkuros | 256875 | - |
dc.identifier.volume | 33 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 552 | - |
dc.identifier.epage | 565 | - |
dc.identifier.isi | WOS:000363663200007 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0735-0015 | - |