Long memory and volatility clustering: is the empirical evidence consistent across stock markets?
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://ciencia.iscte-iul.pt/id/ci-pub-14585 http://hdl.handle.net/10071/13988 |
Resumo: | Long memory and volatility clustering are two stylized facts frequently related to financial markets. Traditionally, these phenomena have been studied based on conditionally heteroscedastic models like ARCH, GARCH, IGARCH and FIGARCH, inter alia. One advantage of these models is their ability to capture nonlinear dynamics. Another interesting manner to study the volatility phenomenon is by using measures based on the concept of entropy. In this paper we investigate the long memory and volatility clustering for the SP 500, NASDAQ 100 and Stoxx 50 indexes in order to compare the US and European Markets. Additionally, we compare the results from conditionally heteroscedastic models with those from the entropy measures. In the latter, we examine Shannon entropy, Renyi entropy and Tsallis entropy. The results corroborate the previous evidence of nonlinear dynamics in the time series considered. |
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Long memory and volatility clustering: is the empirical evidence consistent across stock markets?Long memoryVolatility clusteringARCH type modelsNonlinear dynamicsEntropyLong memory and volatility clustering are two stylized facts frequently related to financial markets. Traditionally, these phenomena have been studied based on conditionally heteroscedastic models like ARCH, GARCH, IGARCH and FIGARCH, inter alia. One advantage of these models is their ability to capture nonlinear dynamics. Another interesting manner to study the volatility phenomenon is by using measures based on the concept of entropy. In this paper we investigate the long memory and volatility clustering for the SP 500, NASDAQ 100 and Stoxx 50 indexes in order to compare the US and European Markets. Additionally, we compare the results from conditionally heteroscedastic models with those from the entropy measures. In the latter, we examine Shannon entropy, Renyi entropy and Tsallis entropy. The results corroborate the previous evidence of nonlinear dynamics in the time series considered.Elsevier2017-07-13T10:08:24Z2008-01-01T00:00:00Z20082017-07-13T10:07:15Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://ciencia.iscte-iul.pt/id/ci-pub-14585http://hdl.handle.net/10071/13988eng0378-437110.1016/j.physa.2008.01.046Bentes, S. R.Menezes, R.Mendes, D. A.info: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:RCAAP2023-11-09T17:25:20Zoai:repositorio.iscte-iul.pt:10071/13988Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:11:29.272771Repositório 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 |
Long memory and volatility clustering: is the empirical evidence consistent across stock markets? |
title |
Long memory and volatility clustering: is the empirical evidence consistent across stock markets? |
spellingShingle |
Long memory and volatility clustering: is the empirical evidence consistent across stock markets? Bentes, S. R. Long memory Volatility clustering ARCH type models Nonlinear dynamics Entropy |
title_short |
Long memory and volatility clustering: is the empirical evidence consistent across stock markets? |
title_full |
Long memory and volatility clustering: is the empirical evidence consistent across stock markets? |
title_fullStr |
Long memory and volatility clustering: is the empirical evidence consistent across stock markets? |
title_full_unstemmed |
Long memory and volatility clustering: is the empirical evidence consistent across stock markets? |
title_sort |
Long memory and volatility clustering: is the empirical evidence consistent across stock markets? |
author |
Bentes, S. R. |
author_facet |
Bentes, S. R. Menezes, R. Mendes, D. A. |
author_role |
author |
author2 |
Menezes, R. Mendes, D. A. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Bentes, S. R. Menezes, R. Mendes, D. A. |
dc.subject.por.fl_str_mv |
Long memory Volatility clustering ARCH type models Nonlinear dynamics Entropy |
topic |
Long memory Volatility clustering ARCH type models Nonlinear dynamics Entropy |
description |
Long memory and volatility clustering are two stylized facts frequently related to financial markets. Traditionally, these phenomena have been studied based on conditionally heteroscedastic models like ARCH, GARCH, IGARCH and FIGARCH, inter alia. One advantage of these models is their ability to capture nonlinear dynamics. Another interesting manner to study the volatility phenomenon is by using measures based on the concept of entropy. In this paper we investigate the long memory and volatility clustering for the SP 500, NASDAQ 100 and Stoxx 50 indexes in order to compare the US and European Markets. Additionally, we compare the results from conditionally heteroscedastic models with those from the entropy measures. In the latter, we examine Shannon entropy, Renyi entropy and Tsallis entropy. The results corroborate the previous evidence of nonlinear dynamics in the time series considered. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-01-01T00:00:00Z 2008 2017-07-13T10:08:24Z 2017-07-13T10:07:15Z |
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 |
https://ciencia.iscte-iul.pt/id/ci-pub-14585 http://hdl.handle.net/10071/13988 |
url |
https://ciencia.iscte-iul.pt/id/ci-pub-14585 http://hdl.handle.net/10071/13988 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0378-4371 10.1016/j.physa.2008.01.046 |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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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1799134669127548928 |