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dc.contributor.authorAlfreedi, Ajab A.en_US
dc.contributor.authorIsa, Zaidien_US
dc.contributor.authorHassan, Abuen_US
dc.date.accessioned2012-03-22en_US
dc.date.accessioned2012-05-22T16:45:43Z-
dc.date.available2012-05-22T16:45:43Z-
dc.date.issued2012en_US
dc.identifier.citationJournal of Statistical and Econometric Methods 2241-0376 International Scientific Press 1 2012 1 43-76en_US
dc.identifier.urihttp://hdl.handle.net/10419/58012-
dc.description.abstractIn this study, we have investigated GCC stock market volatilities exploiting a number of asymmetric models (EGARCH, ICSS-EGARCH, GJR-GARCH, and ICSS-GJR-GARCH).This paper uses the weekly data over the period 2003-2010. The ICSS-EGARCH and ICSS-GJR-GARCH models take into account the discrete regime shifts in stochastic errors. The finding supports the widely accepted view that accounting for the regime shifts detected by the iterated cumulative sums of squares (ICSS) algorithm in the variance equations overcomes the overestimation of volatility persistence. In addition, we have discovered that the sudden changes are generally associated with global, regional, and domestic economic as well as political events. Importantly, the asymmetric model estimations use normal as well as heavy-tailed conditional densities.en_US
dc.language.isoengen_US
dc.publisherInternational Scientific Pressen_US
dc.subject.ddc330en_US
dc.subject.keywordasymmetric modelsen_US
dc.subject.keywordICSSen_US
dc.subject.keywordEGARCHen_US
dc.subject.keywordGJR-GARCHen_US
dc.subject.keywordheavy-tailed process : GCC stock marketen_US
dc.titleRegime shifts in asymmetric GARCH models assuming heavy-tailed distribution: Evidence from GCC stock marketsen_US
dc.typeArticleen_US
dc.identifier.ppn689054440en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:Journal of Statistical and Econometric Methods

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