Forecast density combinations of dynamic models and data driven portfolio strategies

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • N. Baştürk, Maastricht University
  • ,
  • A. Borowska, Tinbergen Institute Amsterdam, Vrije Universiteit Amsterdam, University of Glasgow
  • ,
  • S. Grassi
  • L. Hoogerheide, Tinbergen Institute Amsterdam, Vrije Universiteit Amsterdam
  • ,
  • H. K. van Dijk, Tinbergen Institute Amsterdam, Erasmus University Rotterdam, Norges Bank

A dynamic asset-allocation model is specified in probabilistic terms as a combination of return distributions resulting from multiple pairs of dynamic models and portfolio strategies based on momentum patterns in US industry returns. The nonlinear state space representation of the model allows efficient and robust simulation-based Bayesian inference using a novel non-linear filter. Combination weights can be cross-correlated and correlated over time using feedback mechanisms. Diagnostic analysis gives insight into model and strategy misspecification. Empirical results show that a smaller flexible model-strategy combination performs better in terms of expected return and risk than a larger basic model-strategy combination. Dynamic patterns in combination weights and diagnostic learning provide useful signals for improved modeling and policy, in particular, from a risk-management perspective.

Original languageEnglish
JournalJournal of Econometrics
Pages (from-to)170-186
Number of pages17
Publication statusPublished - May 2019
Externally publishedYes

    Research areas

  • Bayes estimates, Filtering methods, Forecast combination, Momentum strategy

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