A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process

One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC) specification. However, the underlying stochastic process to derive DCC has not yet been established, which has made problematic the derivation of asymptotic properties of the Quasi...

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Detalles Bibliográficos
Autores: Hafner, Christian M., McAleer, Michael
Tipo de recurso: informe técnico
Fecha de publicación:2014
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/41614
Acceso en línea:https://hdl.handle.net/20.500.14352/41614
Access Level:acceso abierto
Palabra clave:C22
C52
C58
G32
Dynamic conditional correlation
Dynamic conditional Covariance
Vector random. coefficient moving average
stationarity
invertibility
asymptotic properties.
Econometría (Economía)
5302 Econometría
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oai_identifier_str oai:docta.ucm.es:20.500.14352/41614
network_acronym_str ES
network_name_str España
repository_id_str
spelling A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average ProcessHafner, Christian M.McAleer, MichaelC22C52C58G32Dynamic conditional correlationDynamic conditional CovarianceVector random. coefficient moving averagestationarityinvertibilityasymptotic properties.Econometría (Economía)5302 EconometríaOne of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC) specification. However, the underlying stochastic process to derive DCC has not yet been established, which has made problematic the derivation of asymptotic properties of the Quasi-Maximum Likelihood Estimators. The paper shows that the DCC model can be obtained from a vector random coefficient moving average process, and derives the stationarity and invertibility conditions. The derivation of DCC from a vector random coefficient moving average process raises three important issues: (i) demonstrates that DCC is, in fact, a dynamic conditional covariance model of the returns shocks rather than a dynamic conditional correlation model; (ii) provides the motivation, which is presently missing, for standardization of the conditional covariance model to obtain the conditional correlation model; and (iii) shows that the appropriate ARCH or GARCH model for DCC is based on the standardized shocks rather than the returns shocks. The derivation of the regularity conditions should subsequently lead to a solid statistical foundation for the estimates of the DCC parameters.Universidad Complutense de Madrid20142014-07-0120142014-07-01technical reporthttp://purl.org/coar/resource_type/c_18ghinfo:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/20.500.14352/41614reponame: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:20.500.14352/416142026-06-02T12:44:21Z
dc.title.none.fl_str_mv A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process
title A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process
spellingShingle A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process
Hafner, Christian M.
C22
C52
C58
G32
Dynamic conditional correlation
Dynamic conditional Covariance
Vector random. coefficient moving average
stationarity
invertibility
asymptotic properties.
Econometría (Economía)
5302 Econometría
title_short A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process
title_full A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process
title_fullStr A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process
title_full_unstemmed A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process
title_sort A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process
dc.creator.none.fl_str_mv Hafner, Christian M.
McAleer, Michael
author Hafner, Christian M.
author_facet Hafner, Christian M.
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 C22
C52
C58
G32
Dynamic conditional correlation
Dynamic conditional Covariance
Vector random. coefficient moving average
stationarity
invertibility
asymptotic properties.
Econometría (Economía)
5302 Econometría
topic C22
C52
C58
G32
Dynamic conditional correlation
Dynamic conditional Covariance
Vector random. coefficient moving average
stationarity
invertibility
asymptotic properties.
Econometría (Economía)
5302 Econometría
description One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC) specification. However, the underlying stochastic process to derive DCC has not yet been established, which has made problematic the derivation of asymptotic properties of the Quasi-Maximum Likelihood Estimators. The paper shows that the DCC model can be obtained from a vector random coefficient moving average process, and derives the stationarity and invertibility conditions. The derivation of DCC from a vector random coefficient moving average process raises three important issues: (i) demonstrates that DCC is, in fact, a dynamic conditional covariance model of the returns shocks rather than a dynamic conditional correlation model; (ii) provides the motivation, which is presently missing, for standardization of the conditional covariance model to obtain the conditional correlation model; and (iii) shows that the appropriate ARCH or GARCH model for DCC is based on the standardized shocks rather than the returns shocks. The derivation of the regularity conditions should subsequently lead to a solid statistical foundation for the estimates of the DCC parameters.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-07-01
2014
2014-07-01
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/20.500.14352/41614
url https://hdl.handle.net/20.500.14352/41614
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
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score 15.811543

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