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.
# 18-076/III (2018-10-14)
- Nalan Basturk, Maastricht University; Agnieszka Borowska, VU University Amsterdam; Stefano Grassi, University of Rome, Tor Vergata; Lennart (L.F.) Hoogerheide, VU University Amsterdam; Herman (H.K.) van Dijk, Erasmus University Rotterdam
- forecast combination, momentum strategy, filtering methods, Bayes estimates
- JEL codes:
- C10, C11, C15