Forecast density combinations of dynamic models and data driven portfolio strategies

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Standard

Forecast density combinations of dynamic models and data driven portfolio strategies. / Baştürk, N.; Borowska, A.; Grassi, S.; Hoogerheide, L.; van Dijk, H. K.

I: Journal of Econometrics, Bind 210, Nr. 1, 05.2019, s. 170-186.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Baştürk, N, Borowska, A, Grassi, S, Hoogerheide, L & van Dijk, HK 2019, 'Forecast density combinations of dynamic models and data driven portfolio strategies', Journal of Econometrics, bind 210, nr. 1, s. 170-186. https://doi.org/10.1016/j.jeconom.2018.11.011

APA

Baştürk, N., Borowska, A., Grassi, S., Hoogerheide, L., & van Dijk, H. K. (2019). Forecast density combinations of dynamic models and data driven portfolio strategies. Journal of Econometrics, 210(1), 170-186. https://doi.org/10.1016/j.jeconom.2018.11.011

CBE

Baştürk N, Borowska A, Grassi S, Hoogerheide L, van Dijk HK. 2019. Forecast density combinations of dynamic models and data driven portfolio strategies. Journal of Econometrics. 210(1):170-186. https://doi.org/10.1016/j.jeconom.2018.11.011

MLA

Vancouver

Baştürk N, Borowska A, Grassi S, Hoogerheide L, van Dijk HK. Forecast density combinations of dynamic models and data driven portfolio strategies. Journal of Econometrics. 2019 maj;210(1):170-186. https://doi.org/10.1016/j.jeconom.2018.11.011

Author

Baştürk, N. ; Borowska, A. ; Grassi, S. ; Hoogerheide, L. ; van Dijk, H. K. / Forecast density combinations of dynamic models and data driven portfolio strategies. I: Journal of Econometrics. 2019 ; Bind 210, Nr. 1. s. 170-186.

Bibtex

@article{b059bcd0066e49e3b5fc7bd9de04aaeb,
title = "Forecast density combinations of dynamic models and data driven portfolio strategies",
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 = "Bayes estimates, Filtering methods, Forecast combination, Momentum strategy",
author = "N. Ba{\c s}t{\"u}rk and A. Borowska and S. Grassi and L. Hoogerheide and {van Dijk}, {H. K.}",
year = "2019",
month = may,
doi = "10.1016/j.jeconom.2018.11.011",
language = "English",
volume = "210",
pages = "170--186",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "1",

}

RIS

TY - JOUR

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

AU - Baştürk, N.

AU - Borowska, A.

AU - Grassi, S.

AU - Hoogerheide, L.

AU - van Dijk, H. K.

PY - 2019/5

Y1 - 2019/5

N2 - 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.

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.

KW - Bayes estimates

KW - Filtering methods

KW - Forecast combination

KW - Momentum strategy

UR - http://www.scopus.com/inward/record.url?scp=85057214511&partnerID=8YFLogxK

U2 - 10.1016/j.jeconom.2018.11.011

DO - 10.1016/j.jeconom.2018.11.011

M3 - Journal article

AN - SCOPUS:85057214511

VL - 210

SP - 170

EP - 186

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 1

ER -