Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/185595 
Year of Publication: 
2018
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2018-076/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
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 modelling and policy, in particular, from a risk-management perspective.
Subjects: 
forecast combination
momentum strategy
filtering methods
Bayes estimates
JEL: 
C10
C11
C15
Document Type: 
Working Paper

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