Forecast density combinations of dynamic models and data driven portfolio strategies

Baştürk, N., Borowska, A. , Grassi, S., Hoogerheide, L. and van Dijk, H.K. (2019) Forecast density combinations of dynamic models and data driven portfolio strategies. Journal of Econometrics, 210, pp. 170-186. (doi:10.1016/j.jeconom.2018.11.011)

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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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Borowska, Dr Agnieszka
Authors: Baştürk, N., Borowska, A., Grassi, S., Hoogerheide, L., and van Dijk, H.K.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Journal of Econometrics
Publisher:Elsevier
ISSN:0304-4076
ISSN (Online):1872-6895
Published Online:12 November 2018

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