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dc.contributor.authorGerlach, Richard
dc.contributor.authorChen, Cathy W.S.
dc.contributor.authorLin, Liou-Yan
dc.date.accessioned2012-03-09
dc.date.available2012-03-09
dc.date.issued2012-01-01
dc.identifier.urihttp://hdl.handle.net/2123/8169
dc.description.abstractBayesian semi-parametric estimation has proven effective for quantile estimation in general and specifically in financial Value at Risk forecasting. Expected short-fall is a competing tail risk measure, involving a conditional expectation beyond a quantile, that has recently been semi-parametrically estimated via asymmetric least squares and so-called expectiles. An asymmetric Gaussian density is proposed allowing a likelihood to be developed that leads to Bayesian semi-parametric estimation and forecasts of expectiles and expected shortfall. Further, the conditional autoregressive expectile class of model is generalised to two fully nonlinear families. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation in these families. The proposed models are clearly favoured in an empirical study forecasting eleven financial return series: clear evidence of more accurate expected shortfall forecasting, compared to a range of competing methods is found. Further, the most favoured models are those estimated by Bayesian methods.en_AU
dc.language.isoenen_AU
dc.publisherBusiness Analytics.en_AU
dc.relation.ispartofseriesBAWP-2012-01en_AU
dc.subjectCARE modelen_AU
dc.subjectNonlinearen_AU
dc.subjectAsymmetric Gaussian distributionen_AU
dc.subjectExpected shortfallen_AU
dc.subjectsemi-parametricen_AU
dc.titleBayesian Semi-parametric Expected Shortfall Forecasting in Financial Marketsen_AU
dc.typeWorking Paperen_AU
dc.contributor.departmentDiscipline of Business Analyticsen_AU


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