0000000000280089
AUTHOR
J. Samuel Baixauli-soler
Several risk measures in portfolio selection: Is it worthwhile?
Este articulo aborda el problema de seleccion de carteras empleando tres medidas de riesgo ampliamente utilizadas: varianza o desviacion tipica, Valor en Riesgo (VaR) y Valor en Riesgo Condicional (CVaR). Nuestro principal objetivo es evaluar la relevancia de incluir simultaneamente varias medidas del riesgo, dada la complejidad computacional que supone. La principal contribucion de este articulo es la propuesta de solucion de dos modelos que consideran simultaneamente dos medidas del riesgo muy utilizadas: el modelo de media-varianza-VaR y el modelo media-VaR-CVaR. La inclusion del VaR como uno de los objetivos a minimizar convierte el problema de optimizacion en no convexo, por lo que el …
A naïve approach to speed up portfolio optimization problem using a multiobjective genetic algorithm
a b s t r a c t Genetic algorithms (GAs) are appropriate when investors have the objective of obtaining mean-variance (VaR) efficient frontier as minimising VaR leads to non-convex and non-differential risk-return optimisation problems. However GAs are a time-consuming optimisation technique. In this paper, we propose to use a naive approach consisting of using samples split by quartile of risk to obtain complete efficient frontiers in a reasonable computation time. Our results show that using reduced problems which only consider a quartile of the assets allow us to explore the efficient frontier for a large range of risk values. In particular, the third quartile allows us to obtain efficie…
Minimising value-at-risk in a portfolio optimisation problem using a multi-objective genetic algorithm
[EN] In this paper, we develop a general framework for market risk optimisation that focuses on VaR. The reason for this choice is the complexity and problems associated with risk return optimisation (non-convex and non-differential objective function). Our purpose is to obtain VaR efficient frontiers using a multi-objective genetic algorithm (GA) and to show the potential utility of the algorithm to obtain efficient portfolios when the risk measure does not allow calculating an optimal solution. Furthermore, we measure differences between VaR efficient frontiers and variance efficient frontiers in VaR-return space and we evaluate out-sample capacity of portfolios on both bullish and bearis…