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Cajas, D., (2020). Optimización robusta de portafolios mediante Near Optimal Centering [Tesis, Universidad Nacional de Ingeniería]. http://hdl.handle.net/20.500.14076/21688
Cajas, D., Optimización robusta de portafolios mediante Near Optimal Centering [Tesis]. PE: Universidad Nacional de Ingeniería; 2020. http://hdl.handle.net/20.500.14076/21688
@misc{renati/266715,
title = "Optimización robusta de portafolios mediante Near Optimal Centering",
author = "Cajas Narro, Dany Rusbel",
publisher = "Universidad Nacional de Ingeniería",
year = "2020"
}
Title: Optimización robusta de portafolios mediante Near Optimal Centering
Authors(s): Cajas Narro, Dany Rusbel
Advisor(s): Caparó Coronado, Rafael Jimmy
OCDE field: http://purl.org/pe-repo/ocde/ford#5.02.01
Issue Date: 2020
Institution: Universidad Nacional de Ingeniería
Abstract: La optimización de portafolios es la herramienta más utilizada por la academia para crear portafolios de inversión; sin embargo, la industria de administración de activos se ha alejado de esta herramienta debido a que sufre principalmente de dos problemas: la falta de robustez de los pesos de los activos que componen el portafolio óptimo, debido a que estos pesos son muy sensibles a cambios en los parámetros de entrada; y la herramienta tiende a generar portafolios con pesos extremos o portafolios poco diversificados. En la literatura, existen diversos métodos que intentan resolver ambos problemas; en este trabajo discutiremos seis de estos métodos: resampling, shrinkage, worst case, Black Litterman, risk factors y maximum distance optimization (MDO); y además desarrollaremos una nuevo método llamado near optimal centering (NOC).
El método de resampling intenta obtener portafolios más robustos generando varias muestras para estimar los parámetros de entrada y promediando los pesos que resultan del proceso de optimización de cada muestra. El método de shrinkage intenta obtener portafolios más robustos al hacer más robusta la estimación de los parámetros de entrada. El método de worst case intenta obtener portafolios más robustos al estresar los parámetros de entrada. El método de Black Litterman intenta obtener portafolios más robustos al incorporar los puntos de vista del analista en la estimación de los parámetros de entrada. El método de risk factors intenta obtener portafolios más robustos al basar la estimación de los parámetros de entrada en un modelo de factores especificado por el analista. El método de MDO intenta obtener portafolios más robustos al explorar la región por debajo de la frontera eficiente y hallar portafolios casi óptimos que estén lo más alejados entre sí para luego combinarlos de acuerdo a los objetivos del analista.
Finalmente, en este trabajo se presenta el método NOC, el cual explora la región por debajo de la frontera eficiente y define una región permisible al igual que el método MDO, para luego definir el centro analítico de esta región como el portafolio óptimo. En este trabajo se busca demostrar las ventajas del método NOC respecto a otros métodos de optimización, para ello se comparó 35 métodos de optimización para 4 tamaños de muestra durante 81 meses. Los resultados de los experimentos sugieren que el método NOC mejora las propiedades de robustez y diversificación de los portafolios en una mayor proporción comparado con otros métodos de optimización robusta, sin sacrificar los niveles de desempeño fuera de la muestra.
Portfolio optimization is the most used tool by the academy to create investment portfolios; however, the asset management industry has moved away from this tool because it su_ers mainly of two problems: the lack of robustness of portfolio weights that make up the optimal portfolio, due to that these weights are very sensitive to changes in the input parameters; and the tool tends to generate portfolios with extreme weights or poorly diversi_ed portfolios. In the literature, there are several methods that try to solve both problems; in this paper we will discuss six of these methods: resampling, shrinkage, worst case, Black Litterman, risk factors and maximum distance optimization (MDO); and we will also develop a new method called near optimal centering (NOC). The resampling method attempts to obtain more robust portfolios by generating several samples to estimate the input parameters and averaging the weights that result from the optimization process of each sample. The shrinkage method attempts to obtain more robust portfolios by making the estimation of the input parameters more robust. The worst case method attempts to obtain more robust portfolios by stressing the input parameters. The Black Litterman method attempts to obtain more robust portfolios by incorporating the analyst's views into the estimation of the input parameters. The risk factors method attempts to obtain more robust portfolios by basing the estimation of the input parameters on a factor model speci_ed by the analyst. The MDO method attempts to obtain more robust portfolios by exploring the region below the e_cient frontier and _nding near optimal portfolios that are as far apart as possible and then combine them according to the analyst's objectives. Finally, this paper presents the NOC method, which explores the region below the e_cient frontier and de_nes a permissible region as well as the MDO method, and then de_nes the analytical center of this region as the optimal portfolio. This paper seeks to demonstrate the advantages of the NOC method over other optimization methods, for this purpose 35 optimization methods were compared for 4 sample sizes over 81 months. The results of the experiments suggest that the NOC method improves the robustness and diversi_cation properties of the portfolios in a greater proportion compared to other robust optimization methods, without sacri_cing performance levels outside the sample.
Portfolio optimization is the most used tool by the academy to create investment portfolios; however, the asset management industry has moved away from this tool because it su_ers mainly of two problems: the lack of robustness of portfolio weights that make up the optimal portfolio, due to that these weights are very sensitive to changes in the input parameters; and the tool tends to generate portfolios with extreme weights or poorly diversi_ed portfolios. In the literature, there are several methods that try to solve both problems; in this paper we will discuss six of these methods: resampling, shrinkage, worst case, Black Litterman, risk factors and maximum distance optimization (MDO); and we will also develop a new method called near optimal centering (NOC). The resampling method attempts to obtain more robust portfolios by generating several samples to estimate the input parameters and averaging the weights that result from the optimization process of each sample. The shrinkage method attempts to obtain more robust portfolios by making the estimation of the input parameters more robust. The worst case method attempts to obtain more robust portfolios by stressing the input parameters. The Black Litterman method attempts to obtain more robust portfolios by incorporating the analyst's views into the estimation of the input parameters. The risk factors method attempts to obtain more robust portfolios by basing the estimation of the input parameters on a factor model speci_ed by the analyst. The MDO method attempts to obtain more robust portfolios by exploring the region below the e_cient frontier and _nding near optimal portfolios that are as far apart as possible and then combine them according to the analyst's objectives. Finally, this paper presents the NOC method, which explores the region below the e_cient frontier and de_nes a permissible region as well as the MDO method, and then de_nes the analytical center of this region as the optimal portfolio. This paper seeks to demonstrate the advantages of the NOC method over other optimization methods, for this purpose 35 optimization methods were compared for 4 sample sizes over 81 months. The results of the experiments suggest that the NOC method improves the robustness and diversi_cation properties of the portfolios in a greater proportion compared to other robust optimization methods, without sacri_cing performance levels outside the sample.
Link to repository: http://hdl.handle.net/20.500.14076/21688
Discipline: Ingeniería Económica
Grade or title grantor: Universidad Nacional de Ingeniería. Facultad de Ingeniería Económica, Estadística y Ciencias Sociales
Grade or title: Ingeniero Economista
Juror: Arnao Rondán, Raymundo Ildefonzo; Vento Ortiz, Alfredo Raul; Vásquez Rodríguez, Rafael
Register date: 21-Feb-2022
This item is licensed under a Creative Commons License