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| dc.creator | Aragón Urrego, Daniel | |
| dc.date | 2022-12-14 | |
| dc.date.accessioned | 2023-03-27T17:38:33Z | |
| dc.date.available | 2023-03-27T17:38:33Z | |
| dc.identifier | https://revistas.uexternado.edu.co/index.php/odeon/article/view/8491 | |
| dc.identifier | 10.18601/17941113.n21.06 | |
| dc.identifier.uri | https://biblioteca-repositorio.clacso.edu.ar/handle/CLACSO/230374 | |
| dc.description | This paper presents the Hierarchical Risk Parity (HRP) approach proposed by López de Prado (2016, 2018, 2020) for the construction of optimal investment portfolios using unsupervised learning, hierarchical clustering, which allow overcome some limitations of the Mean-Variance (MV) model, in particular those related to the need to invert the covariance matrix when implementing the CLA algorithm. A sample of 7 assets from the American market is taken, with which an application of the HRP algorithm proposed by López de Prado is carried out, finding that under this model the distribution of assets in different clusters generates improvements in terms of the expected return, as well as of the Sharpe coefficient compared to the results of the Mean-Variance portfolio. | en-US |
| dc.description | Este artículo presenta el enfoque de paridad de riesgo jerárquico (HRP, por sus siglas en inglés) propuesto por López de Prado para la construcción de portafolios óptimos de inversión empleando aprendizaje no supervisado –clustering jerárquico–, que permitan superar algunas limitaciones propias del modelo de Media-Varianza, en particular aquella relacionada con la necesidad de invertir la matriz de covarianzas en el momento de implementar el algoritmo CLA. Se toma una muestra de siete activos del mercado estadounidense, con los cuales se realiza una aplicación del algoritmo HRP propuesto por López de Prado; se encuentra que bajo este modelo la distribución de los activos en diferentes clústeres genera mejoras en términos del retorno esperado, así como del coeficiente de Sharpe en comparación con los resultados del portafolio de media-varianza. | es-ES |
| dc.format | application/pdf | |
| dc.language | spa | |
| dc.publisher | Universidad Externado de Colombia | es-ES |
| dc.relation | https://revistas.uexternado.edu.co/index.php/odeon/article/view/8491/13079 | |
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| dc.relation | /*ref*/Bailey, D. y López de Prado, M. (2012). The Sharpe coefficient efficient frontier. Journal of Risk, 15(2): 3-44. https://doi.org/10.21314/jor.2012.255 | |
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| dc.relation | /*ref*/López de Prado, M. (2016). Building diversified portfolios that outperform out of sam¬ple. The Journal of Portfolio Management, 42(4), 59-69. https://doi.org/10.3905/ jpm.2016.42.4.059 | |
| dc.relation | /*ref*/López de Prado, M. (2018). Advances in financial machine learning. John Wiley & Sons. | |
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| dc.relation | /*ref*/Sharpe, W. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442. https://doi.org/10.1111/j.1540-6261.1964. tb02865.x | |
| dc.relation | /*ref*/Tatsat, H., Puri, S. y Lookabaugh, B. (2020). Machine Learning and Data Science Blueprints for Finance. O’Reilly Media. | |
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| dc.rights | Derechos de autor 2022 Daniel Aragón Urrego | es-ES |
| dc.rights | http://creativecommons.org/licenses/by-nc-sa/4.0 | es-ES |
| dc.source | Odeon; No. 21 (2021): Julio-Diciembre; 105-124 | en-US |
| dc.source | Revista ODEON; Núm. 21 (2021): Julio-Diciembre; 105-124 | es-ES |
| dc.source | 2346-2140 | |
| dc.source | 1794-1113 | |
| dc.subject | Optimal portfolio; | en-US |
| dc.subject | risk parity; | en-US |
| dc.subject | clustering | en-US |
| dc.subject | portafolio óptimo; | es-ES |
| dc.subject | clustering | es-ES |
| dc.subject | paridad de riesgo | es-ES |
| dc.title | Hierarchical Risk Parity: Approach to the method and application for the American market | en-US |
| dc.title | Paridad de riesgo jerárquico: aproximación al método y aplicación para el mercado estadounidense | es-ES |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion |
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