Identifying common dynamic features in stock returns
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.5/27670 |
Resumo: | This paper proposes volatility and spectral based methods for the cluster analysis of stock returns. Using the information about both the estimated parameters in the threshold GARCH (or TGARCH) equation and the periodogram of the squared returns, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates. We employ these techniques to investigate the similarities and dissimilarities between the ‘blue-chip’ stocks used to compute the Dow Jones Industrial Average (DJIA) index. |
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Identifying common dynamic features in stock returnsAsymmetric EffectsCluster AnalysisDJIA Stock ReturnsPeriodogramThreshold GARCH ModelVolatilityThis paper proposes volatility and spectral based methods for the cluster analysis of stock returns. Using the information about both the estimated parameters in the threshold GARCH (or TGARCH) equation and the periodogram of the squared returns, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates. We employ these techniques to investigate the similarities and dissimilarities between the ‘blue-chip’ stocks used to compute the Dow Jones Industrial Average (DJIA) index.Taylor & FrancisRepositório da Universidade de LisboaCaiado, JorgeCrato, Nuno2023-04-28T09:31:38Z20102010-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/27670engCaiado, Jorge and Nuno Crato .(2010). “Identifying common dynamic features in stock returns”. Quantitative Finance, Vol. 10, No. 7: pp. 797–807. (Search PDF in 2023)1469–7696 (Online)10.1080/14697680903567152info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-04-30T01:30:55Zoai:www.repository.utl.pt:10400.5/27670Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:50:28.853160Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Identifying common dynamic features in stock returns |
title |
Identifying common dynamic features in stock returns |
spellingShingle |
Identifying common dynamic features in stock returns Caiado, Jorge Asymmetric Effects Cluster Analysis DJIA Stock Returns Periodogram Threshold GARCH Model Volatility |
title_short |
Identifying common dynamic features in stock returns |
title_full |
Identifying common dynamic features in stock returns |
title_fullStr |
Identifying common dynamic features in stock returns |
title_full_unstemmed |
Identifying common dynamic features in stock returns |
title_sort |
Identifying common dynamic features in stock returns |
author |
Caiado, Jorge |
author_facet |
Caiado, Jorge Crato, Nuno |
author_role |
author |
author2 |
Crato, Nuno |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Caiado, Jorge Crato, Nuno |
dc.subject.por.fl_str_mv |
Asymmetric Effects Cluster Analysis DJIA Stock Returns Periodogram Threshold GARCH Model Volatility |
topic |
Asymmetric Effects Cluster Analysis DJIA Stock Returns Periodogram Threshold GARCH Model Volatility |
description |
This paper proposes volatility and spectral based methods for the cluster analysis of stock returns. Using the information about both the estimated parameters in the threshold GARCH (or TGARCH) equation and the periodogram of the squared returns, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates. We employ these techniques to investigate the similarities and dissimilarities between the ‘blue-chip’ stocks used to compute the Dow Jones Industrial Average (DJIA) index. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010 2010-01-01T00:00:00Z 2023-04-28T09:31:38Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.5/27670 |
url |
http://hdl.handle.net/10400.5/27670 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Caiado, Jorge and Nuno Crato .(2010). “Identifying common dynamic features in stock returns”. Quantitative Finance, Vol. 10, No. 7: pp. 797–807. (Search PDF in 2023) 1469–7696 (Online) 10.1080/14697680903567152 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Taylor & Francis |
publisher.none.fl_str_mv |
Taylor & Francis |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799131584675184640 |