Higher Moment Constraints for Predictive Density Combinations
Field | Value | Language |
dc.contributor.author | Pauwels, Laurent | |
dc.contributor.author | Radchenko, Peter | |
dc.contributor.author | Vasnev, Andrey | |
dc.date.accessioned | 2019-03-19 | |
dc.date.available | 2019-03-19 | |
dc.date.issued | 2019-03-19 | |
dc.identifier.uri | http://hdl.handle.net/2123/20175 | |
dc.description.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. | en_AU |
dc.language.iso | en_US | en_AU |
dc.publisher | Business Analytics. | en_AU |
dc.relation.ispartofseries | BAWP-2019-01 | en_AU |
dc.subject | Forecast combination | en_AU |
dc.subject | Predictive densities | en_AU |
dc.subject | Optimal weights | en_AU |
dc.subject | Skewness | en_AU |
dc.subject | Kurtosis | en_AU |
dc.title | Higher Moment Constraints for Predictive Density Combinations | en_AU |
dc.type | Working Paper | en_AU |
dc.contributor.department | Disciipline of Business Analytics, The University of Sydney Business School | en_AU |
Associated file/s
Associated collections