Ten Things You Should Know About DCC

The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: D...

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Detalles Bibliográficos
Autores: Caporin, Massimiliano, McAleer, Michael
Tipo de recurso: informe técnico
Fecha de publicación:2013
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/41466
Acceso en línea:https://hdl.handle.net/20.500.14352/41466
Access Level:acceso abierto
Palabra clave:C18
C32
C58
G17
DCC
BEKK
GARCC
Stated representation
Derived model
Conditional covariances
Conditional correlations
Regularity conditions
Moments
Two step estimators
Assumed properties
Asymptotic properties
Filter
Diagnostic check.
Econometría (Economía)
5302 Econometría
id ES_1b7973df978081ad2a24bbcd3576358b
oai_identifier_str oai:docta.ucm.es:20.500.14352/41466
network_acronym_str ES
network_name_str España
repository_id_str
spelling Ten Things You Should Know About DCCCaporin, MassimilianoMcAleer, MichaelC18C32C58G17DCCBEKKGARCCStated representationDerived modelConditional covariancesConditional correlationsRegularity conditionsMomentsTwo step estimatorsAssumed propertiesAsymptotic propertiesFilterDiagnostic check.Econometría (Economía)5302 EconometríaThe purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of GARCC, which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal BEKK in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.Universidad Complutense de Madrid20132013-03-0120132013-03-01technical reporthttp://purl.org/coar/resource_type/c_18ghinfo:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/20.500.14352/41466reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial 3.0 Españahttps://creativecommons.org/licenses/by-nc/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/414662026-06-02T12:44:21Z
dc.title.none.fl_str_mv Ten Things You Should Know About DCC
title Ten Things You Should Know About DCC
spellingShingle Ten Things You Should Know About DCC
Caporin, Massimiliano
C18
C32
C58
G17
DCC
BEKK
GARCC
Stated representation
Derived model
Conditional covariances
Conditional correlations
Regularity conditions
Moments
Two step estimators
Assumed properties
Asymptotic properties
Filter
Diagnostic check.
Econometría (Economía)
5302 Econometría
title_short Ten Things You Should Know About DCC
title_full Ten Things You Should Know About DCC
title_fullStr Ten Things You Should Know About DCC
title_full_unstemmed Ten Things You Should Know About DCC
title_sort Ten Things You Should Know About DCC
dc.creator.none.fl_str_mv Caporin, Massimiliano
McAleer, Michael
author Caporin, Massimiliano
author_facet Caporin, Massimiliano
McAleer, Michael
author_role author
author2 McAleer, Michael
author2_role author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv C18
C32
C58
G17
DCC
BEKK
GARCC
Stated representation
Derived model
Conditional covariances
Conditional correlations
Regularity conditions
Moments
Two step estimators
Assumed properties
Asymptotic properties
Filter
Diagnostic check.
Econometría (Economía)
5302 Econometría
topic C18
C32
C58
G17
DCC
BEKK
GARCC
Stated representation
Derived model
Conditional covariances
Conditional correlations
Regularity conditions
Moments
Two step estimators
Assumed properties
Asymptotic properties
Filter
Diagnostic check.
Econometría (Economía)
5302 Econometría
description The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of GARCC, which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal BEKK in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.
publishDate 2013
dc.date.none.fl_str_mv 2013
2013-03-01
2013
2013-03-01
dc.type.none.fl_str_mv technical report
http://purl.org/coar/resource_type/c_18gh
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/41466
url https://hdl.handle.net/20.500.14352/41466
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
Atribución-NoComercial 3.0 España
https://creativecommons.org/licenses/by-nc/3.0/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
Atribución-NoComercial 3.0 España
https://creativecommons.org/licenses/by-nc/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869404167945060352
score 15.812455

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