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dc.contributor.authorContino, Christian
dc.contributor.authorGerlach, Richard
dc.date.accessioned2014-10-09T23:07:39Z
dc.date.available2014-10-09T23:07:39Z
dc.date.issued2014-10-10
dc.identifier.urihttp://hdl.handle.net/2123/12060
dc.description.abstractA Realised Volatility GARCH model is developed within a Bayesian framework for the purpose of forecasting Value at Risk and Conditional Value at Risk. Student-t and Skewed Student-t return distributions are combined with Gaussian and Student-t distributions in the measurement equation in a GARCH framework to forecast tail risk in eight international equity index markets over a four year period. Three Realised Volatility proxies are considered within this framework. Realised Volatility GARCH models show a marked improvement compared to ordinary GARCH for both Value at Risk and Conditional Value at Risk forecasting. This improvement is consistent across a variety of data, volatility model speci_cations and distributions, and demonstrates that Realised Volatility is superior when producing volatility forecasts. Realised Volatility models implementing a Skewed Student-t distribution for returns in the GARCH equation are favoured.en_AU
dc.language.isoen_AUen_AU
dc.publisherBusiness Analytics.
dc.relation.ispartofseriesBAWP-2014-05en_AU
dc.subjectRealised Volatilityen_AU
dc.subjectGARCHen_AU
dc.subjectValue-at-Risken_AU
dc.subjectCVaRen_AU
dc.subjectHigh-Frequency Dataen_AU
dc.subjectExpected Shortfallen_AU
dc.subjectRisk Managementen_AU
dc.titleBayesian Tail Risk Forecasting using Realised GARCHen_AU
dc.typeWorking Paperen_AU
dc.contributor.departmentDiscipline of Business Analyticsen_AU


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