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Modelling dynamic interdependence in nonstationary variances with an application to carbon markets

dc.contributor.authorCampos-Martins, Susana
dc.contributor.authorAmado, Cristina
dc.date.accessioned2025-07-16T14:28:57Z
dc.date.available2025-07-16T14:28:57Z
dc.date.issued2025-04
dc.description.abstractIn this paper we propose a multivariate generalisation of the multiplicative decomposition of the volatility within the class of conditional correlation GARCH models. The GARCH variance equations are multiplicatively decomposed into a deterministic nonstationary component describing the long-run movements in volatility and a short-run dynamic component allowing for volatility interactions across markets or assets. The conditional correlations are assumed to be time-invariant in its simplest form or generalised into a flexible dynamic parameterisation. Parameters of the model are estimated equation-by-equation by maximum likelihood applying the maximisation by parts algorithm to the variance equations, and thereafter to the structure of conditional correlations. An empirical application using carbon markets data illustrates the usefulness of the model. Our results suggest that, after modelling the variance equations accordingly, we find evidence that the transmission mechanism of shocks is supported by the presence of dynamic interdependence in variances robust to nonstationarity.eng
dc.identifier.doi10.1016/j.jedc.2025.105062
dc.identifier.eid85216983709
dc.identifier.issn0165-1889
dc.identifier.urihttp://hdl.handle.net/10400.14/53947
dc.identifier.wos001422702300001
dc.language.isoeng
dc.peerreviewedyes
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectLagrange multiplier test
dc.subjectNonstationarity
dc.subjectShort and long-term volatility
dc.subjectVariance interactions
dc.titleModelling dynamic interdependence in nonstationary variances with an application to carbon marketseng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.titleJournal of Economic Dynamics and Control
oaire.citation.volume173
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa

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