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  1. Regularized maximum diversification investment strategy
    Author: Koné, N'Golo
    Published: 1-2021
    Publisher:  Department of Economics, Queen's University, Kingston, Ontario, Canada

    The maximum diversification portfolio as defined by Choueifaty (2011) depends on the vector of asset volatilities and the inverse of the covariance matrix of the asset return. In practice, these two quantities need to be replaced by their sample... more

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    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 216
    Inter-library loan:
    No inter-library loan

     

    The maximum diversification portfolio as defined by Choueifaty (2011) depends on the vector of asset volatilities and the inverse of the covariance matrix of the asset return. In practice, these two quantities need to be replaced by their sample statistics. The estimation error associated with the use of these sample statistics may be amplified due to (near) singularity of the covariance matrix, in financial markets with many assets. This in turn may lead to the selection of portfolios that are far from the optimal regarding standard portfolio performance measures of the financial market. To address this problem, we investigate three regularization techniques, including the ridge, the spectral cut-off, and the Landweber-Fridman approaches in order to stabilize the inverse of the covariance matrix. These regularization schemes involve a tuning parameter that needs to be chosen. In light of this fact, we propose a data-driven method for selecting the tuning parameter. We show that the selected portfolio by regularization is asymptotically efficient with respect to the diversification ratio. In empirical and Monte Carlo experiments, the resulting regularized rules are compared to several strategies, such as the most diversified portfolio, the target portfolio, the global minimum variance portfolio, and the naive 1/N strategy in terms of in-sample and out-of-sample Sharpe ratio performance, and it is shown that our method yields significant Sharpe ratio improvements.

     
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/230602
    Series: Queen's Economics Department working paper ; no. 1450
    Subjects: Portfolio selection; Maximum diversification; Regularization
    Scope: 1 Online-Ressource (circa 34 Seiten), Illustrationen
  2. Efficient mean-variance portfolio selection by double regularization
    Author: Koné, N'Golo
    Published: 2-2021
    Publisher:  Department of Economics, Queen's University, Kingston, Ontario, Canada

    This paper addresses the estimation issue that exists when estimating the traditional mean-variance portfolio. More precisely, the efficient mean-variance is estimated by a double regularization. These regularization techniques namely the ridge, the... more

    Access:
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 216
    Inter-library loan:
    No inter-library loan

     

    This paper addresses the estimation issue that exists when estimating the traditional mean-variance portfolio. More precisely, the efficient mean-variance is estimated by a double regularization. These regularization techniques namely the ridge, the spectral cut-off, and Landweber-Fridman involve a regularization parameter or penalty term whose optimal value needs to be selected efficiently. A data-driven method has been proposed to select the tuning parameter. We show that the double regularized portfolio guarantees to investors the maximum expected return with the lowest risk. In empirical and Monte Carlo experiments, our double regularized rules are compared to several strategies, such as the traditional regularized portfolios, the new Lasso strategy of Ao, Yingying, and Zheng (2019), and the naive 1/N strategy in terms of in-sample and out-of-sample Sharpe ratio performance, and it is shown that our method yields significant Sharpe ratio improvements and a reduction in the expected utility loss.

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/230605
    Series: Queen's Economics Department working paper ; no. 1453
    Subjects: Portfolio selection; efficient mean-variance analysis; double regularization
    Scope: 1 Online-Ressource (circa 42 Seiten)