Selecting volatility forecasting models for portfolio allocation purposes

R. Becker, A.E. Clements, M.B. Doolan, A.S. Hurn

Research output: Contribution to journalArticlepeer-review


Techniques for evaluating and selecting multivariate volatility forecasts are not yet understood as well as their univariate counterparts. This paper considers the ability of different loss functions to discriminate between a set of competing forecasting models which are subsequently applied in a portfolio allocation context. It is found that a likelihood-based loss function outperforms its competitors, including those based on the given portfolio application. This result indicates that considering the particular application of forecasts is not necessarily the most effective basis on which to select models.
Original languageEnglish
Pages (from-to)849-861
Number of pages12
JournalInternational Journal of Forecasting
Publication statusPublished - Mar 2014


  • Multivariate time series; Loss functions; Evaluating forecasts; Covariance matrix; GARCH models; Model confidence set


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