Higher Moment Constraints for Predictive Density Combinations
Laurent Pauwels (),
Peter Radchenko and
No BAWP-2019-01, Working Papers from University of Sydney Business School, Discipline of Business Analytics
The 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.
Keywords: Forecast combination; Predictive densities; Optimal weights; Skewness; Kurtosis (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:syb:wpbsba:2123/20175
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