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dc.contributor.authorGerlach, Richard
dc.contributor.authorChen, Cathy W.S.
dc.contributor.authorLin, Edward M.H.
dc.date.accessioned2014-09-10
dc.date.available2014-09-10
dc.date.issued2014-09-10
dc.identifier.urihttp://hdl.handle.net/2123/11816
dc.description.abstractMethods for Bayesian testing and assessment of dynamic quantile forecasts are proposed. Specifically, Bayes factor analogues of popular frequentist tests for independence of violations from, and for correct coverage of a time series of, quantile forecasts are developed. To evaluate the relevant marginal likelihoods involved, analytic integration methods are utilised when possible, otherwise multivariate adaptive quadrature methods are employed to estimate the required quantities. The usual Bayesian interval estimate for a proportion is also examined in this context. The size and power properties of the proposed methods are examined via a simulation study, illustrating favourable comparisons both overall and with their frequentist counterparts. An empirical study employs the proposed methods, in comparison with standard tests, to assess the adequacy of a range of forecasting models for Value at Risk (VaR) in several financial market data series.en_AU
dc.language.isoen_USen_AU
dc.publisherBusiness Analytics
dc.relation.ispartofseriesBAWP-2014-04en_AU
dc.subjectBayesian Hypothesis testingen_AU
dc.subjectBayes factoren_AU
dc.subjectasymmetric-Laplace distributionen_AU
dc.subjectValue-at-Risken_AU
dc.subjectquantile regressionen_AU
dc.titleBayesian Assessment of Dynamic Quantile Forecastsen_AU
dc.typeWorking Paperen_AU
dc.contributor.departmentDiscipline of Business Analyticsen_AU


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