Forecast density combinations of dynamic models and data driven portfolio strategies

Nalan Bastürk, Agnieszka Borowska, Stefano Grassi, Lennart Hoogerheide, Herman K. van Dijk

Research output: Contribution to journalArticleAcademicpeer-review

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. (C) 2018 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)170-186
Number of pages17
JournalJournal of Econometrics
Volume210
Issue number1
DOIs
Publication statusPublished - May 2019

Keywords

  • Forecast combination
  • Momentum strategy
  • Filtering methods
  • Bayes estimates
  • HIERARCHICAL MIXTURES
  • BAYESIAN-INFERENCE
  • CROSS-SECTION
  • EFFICIENT
  • SELECTION
  • EXPERTS
  • ALGORITHMS
  • SIMULATION
  • RETURNS

Cite this

Bastürk, Nalan ; Borowska, Agnieszka ; Grassi, Stefano ; Hoogerheide, Lennart ; van Dijk, Herman K. / Forecast density combinations of dynamic models and data driven portfolio strategies. In: Journal of Econometrics. 2019 ; Vol. 210, No. 1. pp. 170-186.
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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. (C) 2018 Elsevier B.V. All rights reserved.",
keywords = "Forecast combination, Momentum strategy, Filtering methods, Bayes estimates, HIERARCHICAL MIXTURES, BAYESIAN-INFERENCE, CROSS-SECTION, EFFICIENT, SELECTION, EXPERTS, ALGORITHMS, SIMULATION, RETURNS",
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Forecast density combinations of dynamic models and data driven portfolio strategies. / Bastürk, Nalan; Borowska, Agnieszka ; Grassi, Stefano; Hoogerheide, Lennart; van Dijk, Herman K.

In: Journal of Econometrics, Vol. 210, No. 1, 05.2019, p. 170-186.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Forecast density combinations of dynamic models and data driven portfolio strategies

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AU - van Dijk, Herman K.

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AB - 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. (C) 2018 Elsevier B.V. All rights reserved.

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