Forecast density combinations of dynamic models and data driven portfolio strategies

N. Baştürk, A. Borowska, S. Grassi, L. Hoogerheide and Herman van Dijk

Journal of Econometrics, 2019, vol. 210, issue 1, 170-186

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.

Keywords: Forecast combination; Momentum strategy; Filtering methods; Bayes estimates (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407618302124
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies (2018)
Working Paper: Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:210:y:2019:i:1:p:170-186

DOI: 10.1016/j.jeconom.2018.11.011

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().