Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies

Nalan Baştürk, Agnieszka Borowska (), Stefano Grassi (), Lennart (L.F.) Hoogerheide () and Herman van Dijk
Additional contact information
Agnieszka Borowska: VU University Amsterdam
Lennart (L.F.) Hoogerheide: VU University Amsterdam

No 18-076/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: forecast combination; momentum strategy; filtering methods; Bayes estimates (search for similar items in EconPapers)
JEL-codes: C10 C11 C15 (search for similar items in EconPapers)
Date: 2018-10-14
New Economics Papers: this item is included in nep-evo
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Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Forecast density combinations of dynamic models and data driven portfolio strategies (2019)
Working Paper: Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies (2018)
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