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dc.contributor.authorReza, Md Ridwan-
dc.date.accessioned2019-09-30T10:43:13Z-
dc.date.available2019-09-30T10:43:13Z-
dc.date.issued2018-
dc.identifier.citationReza, Md Ridwan. (2018). Forecasting volatilities of Dow Jones Islamic Market World Index: new evidence form Markov regime switching GARCH (Master dissertation). INCEIF, Kuala Lumpur. Retrieved from https://ikr.inceif.org/handle/INCEIF/3098en_US
dc.identifier.urihttps://ikr.inceif.org/handle/INCEIF/3098-
dc.description.abstractThe study employs different types ofrelatively novel Markov regime switching GARCH (MRS-GARCH) models along with standard GARCH to identify better models to forecast the conditional volatility of Dow Jones Islamic Market World Index (DJIM). Several statistical and risk-management-based loss functions are employed to evaluate the out-of-sample volatility and Value-at-Risk (VaR) forecast from these models. Our empirical results show that although there is no single model - either of standard GARCH and MRS-GARCH type - is consistently outperforming the others if both the statistical and the risk-management loss functions are considered, overall different asymmetric Markov regime switching GARCH models, namely MRS-EGARCH andMRS-GJR-type models, perform better than any single regime GARCH specification.en_US
dc.languageEnglish-
dc.language.isoengen_US
dc.publisherINCEIFen_US
dc.rights2018. INCEIF-
dc.sourceSGPS-
dc.subjectMarkov regime switching GARCH (MRS-GARCH)en_US
dc.subjectDow Jones Islamic Market World Index (DJIM)en_US
dc.titleForecasting volatilities of Dow Jones Islamic Market World Index: new evidence form Markov regime switching GARCHen_US
dc.typeMasteren_US
ikr.topic.maintopicIslamic capital marketsen_US
ikr.doctypeTheses-
dc.contributor.supervisorDewandaru, Ginanjar-
ikr.licenseAvailable in physical copy only (Call Number: t HG 4551 M478)-
Appears in Collections:Master


There are no files associated with this item.
Full metadata record
DC FieldValueLanguage
dc.contributor.authorReza, Md Ridwan-
dc.date.accessioned2019-09-30T10:43:13Z-
dc.date.available2019-09-30T10:43:13Z-
dc.date.issued2018-
dc.identifier.citationReza, Md Ridwan. (2018). Forecasting volatilities of Dow Jones Islamic Market World Index: new evidence form Markov regime switching GARCH (Master dissertation). INCEIF, Kuala Lumpur. Retrieved from https://ikr.inceif.org/handle/INCEIF/3098en_US
dc.identifier.urihttps://ikr.inceif.org/handle/INCEIF/3098-
dc.description.abstractThe study employs different types ofrelatively novel Markov regime switching GARCH (MRS-GARCH) models along with standard GARCH to identify better models to forecast the conditional volatility of Dow Jones Islamic Market World Index (DJIM). Several statistical and risk-management-based loss functions are employed to evaluate the out-of-sample volatility and Value-at-Risk (VaR) forecast from these models. Our empirical results show that although there is no single model - either of standard GARCH and MRS-GARCH type - is consistently outperforming the others if both the statistical and the risk-management loss functions are considered, overall different asymmetric Markov regime switching GARCH models, namely MRS-EGARCH andMRS-GJR-type models, perform better than any single regime GARCH specification.en_US
dc.languageEnglish-
dc.language.isoengen_US
dc.publisherINCEIFen_US
dc.rights2018. INCEIF-
dc.sourceSGPS-
dc.subjectMarkov regime switching GARCH (MRS-GARCH)en_US
dc.subjectDow Jones Islamic Market World Index (DJIM)en_US
dc.titleForecasting volatilities of Dow Jones Islamic Market World Index: new evidence form Markov regime switching GARCHen_US
dc.typeMasteren_US
ikr.topic.maintopicIslamic capital marketsen_US
ikr.doctypeTheses-
dc.contributor.supervisorDewandaru, Ginanjar-
ikr.licenseAvailable in physical copy only (Call Number: t HG 4551 M478)-
Appears in Collections:Master


There are no files associated with this item.