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Regularized maximum diversification investment strategy
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| Veröffentlicht in: | Econometrics 9(2021), 1/1 vom: März, Seite 1-23 |
|---|---|
| Personen und Körperschaften: | |
| Titel: | Regularized maximum diversification investment strategy/ N'Golo Koné |
| Format: | E-Book-Kapitel |
| Sprache: | Englisch |
| veröffentlicht: |
2021
|
| Gesamtaufnahme: |
: Econometrics, 9(2021), 1/1 vom: März, Seite 1-23
, volume:9 |
| Schlagwörter: | |
| Quelle: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
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| 520 | |a The maximum diversification has been shown in the literature to depend on the vector of asset volatilities and the inverse of the covariance matrix of the asset return covariance matrix. 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. | ||
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| contents | The maximum diversification has been shown in the literature to depend on the vector of asset volatilities and the inverse of the covariance matrix of the asset return covariance matrix. 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. |
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| spelling | Koné, N'Golo VerfasserIn (DE-588)1218119039 (DE-627)1733577416 aut, Regularized maximum diversification investment strategy N'Golo Koné, 2021, Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2, The maximum diversification has been shown in the literature to depend on the vector of asset volatilities and the inverse of the covariance matrix of the asset return covariance matrix. 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., DE-206 Namensnennung 4.0 International CC BY 4.0 cc http://creativecommons.org/licenses/by/4.0/, maximum diversification, portfolio selection, regularization, Aufsatz in Zeitschrift DE-206, Enthalten in Econometrics Basel : MDPI, 2013 9(2021), 1/1 vom: März, Seite 1-23 Online-Ressource (DE-627)74684042X (DE-600)2717594-7 (DE-576)382897196 2225-1146 nnns, volume:9 year:2021 number:1/1 month:03 pages:1-23, https://www.mdpi.com/2225-1146/9/1/1/pdf Verlag kostenfrei, https://doi.org/10.3390/econometrics9010001 Resolving-System kostenfrei, http://hdl.handle.net/10419/247593 Resolving-System kostenfrei, https://doi.org/10.3390/econometrics9010001 LFER, https://www.mdpi.com/2225-1146/9/1/1/pdf LFER, LFER 2021-02-08T21:20:31Z |
| spellingShingle | Koné, N'Golo, Regularized maximum diversification investment strategy, The maximum diversification has been shown in the literature to depend on the vector of asset volatilities and the inverse of the covariance matrix of the asset return covariance matrix. 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., maximum diversification, portfolio selection, regularization, Aufsatz in Zeitschrift |
| title | Regularized maximum diversification investment strategy |
| title_auth | Regularized maximum diversification investment strategy |
| title_full | Regularized maximum diversification investment strategy N'Golo Koné |
| title_fullStr | Regularized maximum diversification investment strategy N'Golo Koné |
| title_full_unstemmed | Regularized maximum diversification investment strategy N'Golo Koné |
| title_in_hierarchy | Regularized maximum diversification investment strategy / N'Golo Koné, |
| title_short | Regularized maximum diversification investment strategy |
| title_sort | regularized maximum diversification investment strategy |
| topic | maximum diversification, portfolio selection, regularization, Aufsatz in Zeitschrift |
| topic_facet | maximum diversification, portfolio selection, regularization, Aufsatz in Zeitschrift |
| url | https://www.mdpi.com/2225-1146/9/1/1/pdf, https://doi.org/10.3390/econometrics9010001, http://hdl.handle.net/10419/247593 |