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Higher Moment Constraints for Predictive Density Combinations

Laurent Pauwels (), Peter Radchenko and Andrey Vasnev

No BAWP-2019-01, Working Papers from University of Sydney Business School, Discipline of Business Analytics

Abstract: 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)
New Economics Papers: this item is included in nep-ecm and nep-for
Date: 2019-03-19
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