Modeling stock markets' volatility using GARCH models with normal, student's t and stable paretian distributions

Detalhes bibliográficos
Autor(a) principal: Curto, J.
Data de Publicação: 2009
Outros Autores: Pinto, J. C., Tavares, G. N.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/5541
Resumo: As GARCH models and stable Paretian distributions have been revisited in the recent past with the papers of Hansen and Lunde (J Appl Econom 20: 873–889, 2005) and Bidarkota and McCulloch (Quant Finance 4: 256–265, 2004), respectively, in this paper we discuss alternative conditional distributional models for the daily returns of the US, German and Portuguese main stock market indexes, considering ARMA-GARCH models driven by Normal, Student’s t and stable Paretian distributed innovations. We find that a GARCH model with stable Paretian innovations fits returns clearly better than the more popular Normal distribution and slightly better than the Student’s t distribution. However, the Student’s t outperforms the Normal and stable Paretian distributions when the out-of-sample density forecasts are considered.
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spelling Modeling stock markets' volatility using GARCH models with normal, student's t and stable paretian distributionsNon-Gaussian distributionsConditional heteroskedasticityAs GARCH models and stable Paretian distributions have been revisited in the recent past with the papers of Hansen and Lunde (J Appl Econom 20: 873–889, 2005) and Bidarkota and McCulloch (Quant Finance 4: 256–265, 2004), respectively, in this paper we discuss alternative conditional distributional models for the daily returns of the US, German and Portuguese main stock market indexes, considering ARMA-GARCH models driven by Normal, Student’s t and stable Paretian distributed innovations. We find that a GARCH model with stable Paretian innovations fits returns clearly better than the more popular Normal distribution and slightly better than the Student’s t distribution. However, the Student’s t outperforms the Normal and stable Paretian distributions when the out-of-sample density forecasts are considered.Springer2013-09-06T14:49:50Z2009-01-01T00:00:00Z20092019-03-26T16:30:47Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/5541eng0932-502610.1007/s00362-007-0080-5Curto, J.Pinto, J. C.Tavares, G. N.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:RCAAP2023-11-09T17:47:43Zoai:repositorio.iscte-iul.pt:10071/5541Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:23:11.454830Repositório 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 Modeling stock markets' volatility using GARCH models with normal, student's t and stable paretian distributions
title Modeling stock markets' volatility using GARCH models with normal, student's t and stable paretian distributions
spellingShingle Modeling stock markets' volatility using GARCH models with normal, student's t and stable paretian distributions
Curto, J.
Non-Gaussian distributions
Conditional heteroskedasticity
title_short Modeling stock markets' volatility using GARCH models with normal, student's t and stable paretian distributions
title_full Modeling stock markets' volatility using GARCH models with normal, student's t and stable paretian distributions
title_fullStr Modeling stock markets' volatility using GARCH models with normal, student's t and stable paretian distributions
title_full_unstemmed Modeling stock markets' volatility using GARCH models with normal, student's t and stable paretian distributions
title_sort Modeling stock markets' volatility using GARCH models with normal, student's t and stable paretian distributions
author Curto, J.
author_facet Curto, J.
Pinto, J. C.
Tavares, G. N.
author_role author
author2 Pinto, J. C.
Tavares, G. N.
author2_role author
author
dc.contributor.author.fl_str_mv Curto, J.
Pinto, J. C.
Tavares, G. N.
dc.subject.por.fl_str_mv Non-Gaussian distributions
Conditional heteroskedasticity
topic Non-Gaussian distributions
Conditional heteroskedasticity
description As GARCH models and stable Paretian distributions have been revisited in the recent past with the papers of Hansen and Lunde (J Appl Econom 20: 873–889, 2005) and Bidarkota and McCulloch (Quant Finance 4: 256–265, 2004), respectively, in this paper we discuss alternative conditional distributional models for the daily returns of the US, German and Portuguese main stock market indexes, considering ARMA-GARCH models driven by Normal, Student’s t and stable Paretian distributed innovations. We find that a GARCH model with stable Paretian innovations fits returns clearly better than the more popular Normal distribution and slightly better than the Student’s t distribution. However, the Student’s t outperforms the Normal and stable Paretian distributions when the out-of-sample density forecasts are considered.
publishDate 2009
dc.date.none.fl_str_mv 2009-01-01T00:00:00Z
2009
2013-09-06T14:49:50Z
2019-03-26T16:30:47Z
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/10071/5541
url http://hdl.handle.net/10071/5541
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0932-5026
10.1007/s00362-007-0080-5
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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