0000000000225201
AUTHOR
Eva Alfaro-cid
showing 4 related works from this author
Hedging foreign exchange rate risk: Multi-currency diversification
2016
Abstract This article proposes a multi-currency cross-hedging strategy that minimizes the exchange risk. The use of derivatives in small and medium-sized enterprises (SMEs) is not common but, despite its complexity, can be interesting for those with international activities. In particular, the reduction in the exchange risk borne through the use of natural multi-currency cross-hedging is measured, considering Conditional Value-at-Risk (CVaR) and Value-at-Risk (VaR) for measuring market risk instead of the variance. CVaR is minimized using linear programmes, while a multiobjective genetic algorithm is designed for minimizing VaR, considering two scenarios for each currency. The results obtai…
Several risk measures in portfolio selection: Is it worthwhile?
2010
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
2012
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
2011
[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…