Optimal Beats Naive Diversification: Asset Allocation Using High-Frequency Data
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https://doi.org/10.3905/jpm.2020.1.185 |
Metadatos
Título
Optimal Beats Naive Diversification: Asset Allocation Using High-Frequency DataFecha de publicación
2020-09-20Editor
Pageant Media LtdISSN
0095-4918Cita bibliográfica
ALEMANY, Nuria; ARAGÓ, Vicent; SALVADOR, Enrique. Optimal Beats Naive Diversification: Asset Allocation Using High-Frequency Data. The Journal of Portfolio Management, 2020, vol. 47, no 1, p. 51-74.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://jpm.pm-research.com/content/early/2020/09/18/jpm.2020.1.185Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
This article evaluates the usefulness of high-frequency data in optimal portfolio choice. The authors use a comprehensive list of major stock indexes and different frequencies of observations. Furthermore, they consider ... [+]
This article evaluates the usefulness of high-frequency data in optimal portfolio choice. The authors use a comprehensive list of major stock indexes and different frequencies of observations. Furthermore, they consider the impact of economic cycles, microstructure noise, and seasonality on performance. Their results show the ability of high-frequency data–based strategies to beat both monthly and daily based strategies and the benchmark equally weighted portfolio, even in presence of transaction costs. The authors also find that the outperformance arises from the reduction in the estimation error of the covariance matrix, which offsets the increase in transaction costs. [-]
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The Journal of Portfolio Management November 2020, jpm.2020.1.185Derechos de acceso
© 2020 Pageant Media Ltd
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