Optimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustas
Robust Portfolio Optimization: Uncertainty Sets and Robust Counterparts
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Los modelos de optimización robusta (OR) han permitido superar las limitaciones del modelo media-varianza (MV), que comprende el enfoque tradicional para la selección de portafolios óptimos de inversión, al incorporar la incertidumbre de los parámetros del modelo (retornos esperados y covarianzas). En este trabajo se presentan los desarrollos de la OR en la teoría de portafolio mediante el enfoque del peor de los casos, a partir del cual se incorporan las formulaciones robustas para el modelo MV, teniendo en cuenta los trabajos de Markowitz y Sharpe. A partir de estas formulaciones, se lleva a cabo una sencilla aplicación en la que se resaltan las ventajas y bondades de las contrapartes robustas frente al modelo MV original. Al final, se presenta una breve discusión de formulaciones adicionales en materia de conjuntos de incertidumbre y otras medidas de desempeño.
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