A general family of autoregressive conditional duration models applied to high-frequency financial data

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorCunha, Danúbia R.-
Autor(es): dc.creatorGabriel, Roberto Vila-
Autor(es): dc.creatorSaulo, Helton-
Autor(es): dc.creatorFernandez, Rodrigo N.-
Data de aceite: dc.date.accessioned2021-10-14T18:32:42Z-
Data de disponibilização: dc.date.available2021-10-14T18:32:42Z-
Data de envio: dc.date.issued2021-05-17-
Data de envio: dc.date.issued2021-05-17-
Data de envio: dc.date.issued2019-
Fonte completa do material: dc.identifierhttps://repositorio.unb.br/handle/10482/40925-
Fonte completa do material: dc.identifierhttps://doi.org/10.3390/jrfm13030045-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/634208-
Descrição: dc.descriptionIn this paper, we propose a general family of Birnbaum–Saunders autoregressive conditional duration (BS-ACD) models based on generalized Birnbaum–Saunders (GBS) distributions, denoted by GBS-ACD. We further generalize these GBS-ACD models by using a Box-Cox transformation with a shape parameter λ to the conditional median dynamics and an asymmetric response to shocks; this is denoted by GBS-AACD. We then carry out a Monte Carlo simulation study to evaluate the performance of the GBS-ACD models. Finally, an illustration of the proposed models is made by using New York stock exchange (NYSE) transaction data.-
Formato: dc.formatapplication/pdf-
Publicador: dc.publisherMDPI-
Direitos: dc.rightsAcesso Aberto-
Direitos: dc.rightsCopyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).-
Palavras-chave: dc.subjectDistribuições Birnbaum-Saunders-
Palavras-chave: dc.subjectModelos autorregressivos-
Palavras-chave: dc.subjectBolsa de valores-
Título: dc.titleA general family of autoregressive conditional duration models applied to high-frequency financial data-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional – UNB

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