Forecast density combinations of dynamic models and data driven portfolio strategies

N. Baştürk*, A. Borowska, S. Grassi, L. Hoogerheide, H. K. van Dijk

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Abstract

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
Pages (from-to)170-186
Number of pages17
JournalJournal of Econometrics
Volume210
Issue number1
Early online date12 Nov 2018
DOIs
Publication statusPublished - May 2019

Keywords

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

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