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Leopoldo Catania

Portfolio Optimisation Under Flexible Dynamic Dependence Modelling

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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Portfolio Optimisation Under Flexible Dynamic Dependence Modelling. / Catania, Leopoldo; Bernardi, Mauro.
In: Journal of Empirical Finance, Vol. 48, No. September, 2018, p. 1-18.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Harvard

Catania, L & Bernardi, M 2018, 'Portfolio Optimisation Under Flexible Dynamic Dependence Modelling', Journal of Empirical Finance, vol. 48, no. September, pp. 1-18. https://doi.org/10.1016/j.jempfin.2018.05.002

APA

Catania, L., & Bernardi, M. (2018). Portfolio Optimisation Under Flexible Dynamic Dependence Modelling. Journal of Empirical Finance, 48(September), 1-18. https://doi.org/10.1016/j.jempfin.2018.05.002

CBE

Catania L, Bernardi M. 2018. Portfolio Optimisation Under Flexible Dynamic Dependence Modelling. Journal of Empirical Finance. 48(September):1-18. https://doi.org/10.1016/j.jempfin.2018.05.002

MLA

Catania, Leopoldo and Mauro Bernardi. "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling". Journal of Empirical Finance. 2018, 48(September). 1-18. https://doi.org/10.1016/j.jempfin.2018.05.002

Vancouver

Catania L, Bernardi M. Portfolio Optimisation Under Flexible Dynamic Dependence Modelling. Journal of Empirical Finance. 2018;48(September):1-18. doi: 10.1016/j.jempfin.2018.05.002

Author

Catania, Leopoldo ; Bernardi, Mauro. / Portfolio Optimisation Under Flexible Dynamic Dependence Modelling. In: Journal of Empirical Finance. 2018 ; Vol. 48, No. September. pp. 1-18.

Bibtex

@article{e264a8cfa4e1488e8ba8d75947cd0d20,
title = "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling",
abstract = "Signals coming from multivariate higher-order conditional moments as well as the information contained in exogenous covariates can be exploited by rational investors to allocate their wealth among different risky investment opportunities. This paper proposes a new flexible dynamic copula model being able to explain and forecast the time-varying shape of large dimensional asset returns distributions. The time-varying dependence is introduced by allowing the dynamic updating equation of the copula correlation parameters to depend on a latent Markov-switching process as well as on exogenous covariates. As a further key ingredient of the model specification, we let the univariate marginals to be driven by an updating mechanism based on the scaled score of the conditional distribution. This framework allows us to introduce time-variation in the conditional moments up to the fourth order. Time-varying moments are then used to build a portfolio allocation strategy that maximises the utility function of a representative rational investor. We empirically assess that the proposed model substantially improves the optimal portfolio allocation with respect to competing alternative investment strategies.",
keywords = "Dynamic conditional score, Dynamic copula, Generalized autoregressive score, Higher order moments, Markov-switching, Portfolio optimisation",
author = "Leopoldo Catania and Mauro Bernardi",
year = "2018",
doi = "10.1016/j.jempfin.2018.05.002",
language = "English",
volume = "48",
pages = "1--18",
journal = "Journal of Empirical Finance",
issn = "0927-5398",
publisher = "Elsevier BV",
number = "September",

}

RIS

TY - JOUR

T1 - Portfolio Optimisation Under Flexible Dynamic Dependence Modelling

AU - Catania, Leopoldo

AU - Bernardi, Mauro

PY - 2018

Y1 - 2018

N2 - Signals coming from multivariate higher-order conditional moments as well as the information contained in exogenous covariates can be exploited by rational investors to allocate their wealth among different risky investment opportunities. This paper proposes a new flexible dynamic copula model being able to explain and forecast the time-varying shape of large dimensional asset returns distributions. The time-varying dependence is introduced by allowing the dynamic updating equation of the copula correlation parameters to depend on a latent Markov-switching process as well as on exogenous covariates. As a further key ingredient of the model specification, we let the univariate marginals to be driven by an updating mechanism based on the scaled score of the conditional distribution. This framework allows us to introduce time-variation in the conditional moments up to the fourth order. Time-varying moments are then used to build a portfolio allocation strategy that maximises the utility function of a representative rational investor. We empirically assess that the proposed model substantially improves the optimal portfolio allocation with respect to competing alternative investment strategies.

AB - Signals coming from multivariate higher-order conditional moments as well as the information contained in exogenous covariates can be exploited by rational investors to allocate their wealth among different risky investment opportunities. This paper proposes a new flexible dynamic copula model being able to explain and forecast the time-varying shape of large dimensional asset returns distributions. The time-varying dependence is introduced by allowing the dynamic updating equation of the copula correlation parameters to depend on a latent Markov-switching process as well as on exogenous covariates. As a further key ingredient of the model specification, we let the univariate marginals to be driven by an updating mechanism based on the scaled score of the conditional distribution. This framework allows us to introduce time-variation in the conditional moments up to the fourth order. Time-varying moments are then used to build a portfolio allocation strategy that maximises the utility function of a representative rational investor. We empirically assess that the proposed model substantially improves the optimal portfolio allocation with respect to competing alternative investment strategies.

KW - Dynamic conditional score

KW - Dynamic copula

KW - Generalized autoregressive score

KW - Higher order moments

KW - Markov-switching

KW - Portfolio optimisation

U2 - 10.1016/j.jempfin.2018.05.002

DO - 10.1016/j.jempfin.2018.05.002

M3 - Journal article

VL - 48

SP - 1

EP - 18

JO - Journal of Empirical Finance

JF - Journal of Empirical Finance

SN - 0927-5398

IS - September

ER -