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dc.contributor.authorPauwels, Laurent
dc.contributor.authorRadchenko, Peter
dc.contributor.authorVasnev, Andrey
dc.date.accessioned2019-03-19T03:00:21Z
dc.date.available2019-03-19T03:00:21Z
dc.date.issued2019-03-19
dc.identifier.urihttp://hdl.handle.net/2123/20175
dc.description.abstractThe majority of financial data exhibit asymmetry and heavy tails, which makes forecasting the entire density critically important. Recently, a forecast combina- tion methodology has been developed to combine predictive densities. We show that combining individual predictive densities that are skewed and/or heavy-tailed results in significantly reduced skewness and kurtosis. We propose a solution to over- come this problem by deriving optimal log score weights under Higher-order Moment Constraints (HMC). The statistical properties of these weights are investigated the- oretically and through a simulation study. Consistency and asymptotic distribution results for the optimal log score weights with and without high moment constraints are derived. An empirical application that uses the S&P 500 daily index returns illustrates that the proposed HMC weight density combinations perform very well relative to other combination methods.en_AU
dc.language.isoen_USen_AU
dc.publisherBusiness Analytics.en_AU
dc.relation.ispartofseriesBAWP-2019-01en_AU
dc.subjectForecast combinationen_AU
dc.subjectPredictive densitiesen_AU
dc.subjectOptimal weightsen_AU
dc.subjectSkewnessen_AU
dc.subjectKurtosisen_AU
dc.titleHigher Moment Constraints for Predictive Density Combinationsen_AU
dc.typeWorking Paperen_AU
dc.contributor.departmentDisciipline of Business Analytics, The University of Sydney Business Schoolen_AU


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