Modelling stock returns with AR-GARCH processes

Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studying in financial and econometric literature as risk models of many financial time s...

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Detalhes bibliográficos
Autores: Ferenstein, Elzbieta, Gasowski, Miroslaw
Tipo de documento: artigo
Data de publicação:2004
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositório:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglês
OAI Identifier:oai:upcommons.upc.edu:2099/3744
Acesso em linha:https://hdl.handle.net/2099/3744
Access Level:Acceso aberto
Palavra-chave:Inference
Mathematical economics
Inferència
Processos estocàstics
Matemàtica financera
Classificació AMS::62 Statistics::62M Inference from stochastic processes
Classificació AMS::91 Game theory, economics, social and behavioral sciences::91B Mathematical economics
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spelling Modelling stock returns with AR-GARCH processesFerenstein, ElzbietaGasowski, MiroslawInferenceMathematical economicsInferènciaProcessos estocàsticsMatemàtica financeraClassificació AMS::62 Statistics::62M Inference from stochastic processesClassificació AMS::91 Game theory, economics, social and behavioral sciences::91B Mathematical economicsFinancial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studying in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to be good models of white noise distributions.Peer ReviewedInstitut d'Estadística de Catalunya20042004-01-0120072007-11-12journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2099/3744reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 2.5 Spainhttp://creativecommons.org/licenses/by-nc-nd/2.5/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099/37442026-05-27T15:37:01Z
dc.title.none.fl_str_mv Modelling stock returns with AR-GARCH processes
title Modelling stock returns with AR-GARCH processes
spellingShingle Modelling stock returns with AR-GARCH processes
Ferenstein, Elzbieta
Inference
Mathematical economics
Inferència
Processos estocàstics
Matemàtica financera
Classificació AMS::62 Statistics::62M Inference from stochastic processes
Classificació AMS::91 Game theory, economics, social and behavioral sciences::91B Mathematical economics
title_short Modelling stock returns with AR-GARCH processes
title_full Modelling stock returns with AR-GARCH processes
title_fullStr Modelling stock returns with AR-GARCH processes
title_full_unstemmed Modelling stock returns with AR-GARCH processes
title_sort Modelling stock returns with AR-GARCH processes
dc.creator.none.fl_str_mv Ferenstein, Elzbieta
Gasowski, Miroslaw
author Ferenstein, Elzbieta
author_facet Ferenstein, Elzbieta
Gasowski, Miroslaw
author_role author
author2 Gasowski, Miroslaw
author2_role author
dc.subject.none.fl_str_mv Inference
Mathematical economics
Inferència
Processos estocàstics
Matemàtica financera
Classificació AMS::62 Statistics::62M Inference from stochastic processes
Classificació AMS::91 Game theory, economics, social and behavioral sciences::91B Mathematical economics
topic Inference
Mathematical economics
Inferència
Processos estocàstics
Matemàtica financera
Classificació AMS::62 Statistics::62M Inference from stochastic processes
Classificació AMS::91 Game theory, economics, social and behavioral sciences::91B Mathematical economics
description Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studying in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to be good models of white noise distributions.
publishDate 2004
dc.date.none.fl_str_mv 2004
2004-01-01
2007
2007-11-12
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2099/3744
url https://hdl.handle.net/2099/3744
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 2.5 Spain
http://creativecommons.org/licenses/by-nc-nd/2.5/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 2.5 Spain
http://creativecommons.org/licenses/by-nc-nd/2.5/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institut d'Estadística de Catalunya
publisher.none.fl_str_mv Institut d'Estadística de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407550810619904
score 15,298079

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