Higher Moment Constraints for Predictive Density Combinations

Laurent Pauwels, Peter Radchenko and Andrey Vasnev

No BAWP-2020-01, 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 combination 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 theoretically 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 combinations; Predictive densities; Moment constraints; Financial data (search for similar items in EconPapers)
JEL-codes: C53 C58 (search for similar items in EconPapers)
Date: 2020-05-01
New Economics Papers: this item is included in nep-for and nep-ore
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Citations: View citations in EconPapers (6)

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https://hdl.handle.net/2123/22140

Related works:
Working Paper: Higher Moment Constraints for Predictive Density Combinations (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:syb:wpbsba:2123/22140

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