AgradecimientosLa realización de esta tesis no hubiese sido posible sin la participación de personas cuya aportación ha facilitado la conclusión de este trabajo. Quiero aprovechar este espacio para mostrar mi sincera gratitud hacia ellos.Agradezco a mis padres su cariño y el haber infundido en mí la moral y el rigor que me guían.A mi hermana por confiar en mí.Al Doctor Fernando García por aceptar en primer lugar la proposición de tesina final de master y por animarme a continuar con un trabajo doctoral. Tengo que agradecerle sus comentarios, direcciones y minuciosas correcciones con las que he podido elaborar adecuadamente este trabajo. Su apoyo y confianza han sido de gran valor no solamente en el desarrollo de la tesis, sino también en el estimulo para seguir creciendo intelectualmente.Al Doctor Francisco Guijarro por introducirme en el mundo de las finanzas cuantitativas y por su cuidadosa dedicación en la dirección del Master en Dirección Financiera y Fiscal.ii Contenido Agradecimientos _______________________________________________________________ iContenido ___________________________________________________________________ ii

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To create efficient funds appealing to a sector of bank clients, the objective of minimizing downside risk is relevant to managers of funds offered by the banks. In this paper, a case focusing on this objective is developed. More precisely, the scope and purpose of the paper is to apply the mean-semivariance efficient frontier model, which is a recent approach to portfolio selection of stocks when the investor is especially interested in the constrained minimization of downside risk measured by the portfolio semivariance. Concerning the opportunity set and observation period, the mean-semivariance efficient frontier model is applied to an actual case of portfolio choice from Dow Jones stocks with daily prices observed over the period 2005-2009. From these daily prices, time series of returns (capital gains weekly computed) are obtained as a piece of basic information. Diversification constraints are established so that each portfolio weight cannot exceed 5 per cent. The results show significant differences between the portfolios obtained by mean-semivariance efficient frontier model and those portfolios of equal expected returns obtained by classical Markowitz mean-variance efficient frontier model. Precise comparisons between them are made, leading to the conclusion that the results are consistent with the objective of reflecting downside risk.

This paper proposes a Compromise Programming (CP) model to help investors decide whether to construct photovoltaic power plants with government financial support. For this purpose, we simulate an agreement between the government, who pursues political prices (guaranteed prices) as low as possible, and the project sponsor who wants returns (stochastic cash flows) as high as possible. The sponsor's decision depends on the positive or negative result of this simulation, the resulting simulated price being compared to the effective guaranteed price established by the country legislation for photovoltaic energy. To undertake the simulation, the CP model articulates variables such as ranges of guaranteed prices, technical characteristics of the plant, expected energy to be generated over the investment life, investment cost, cash flow probabilities, and others. To determine the CP metric, risk

This paper deals with benchmark-based portfolio choice for buy-and-hold strategies of investing. Multiple benchmarks for returns are considered, which is more realistic than taking a unique benchmark -a unique aspiration difficult to select in practice among the various aspirations for returns that the investor has in mind. Portfolio selection with multiple benchmarks leads to a multi-objective problem, which is addressed by mean value -stochastic goal programming. In particular, two benchmarks are considered, which involves two goals. Weights for goals depend on investor's preferences and Arrow's absolute risk aversion coefficients. An efficient frontier of portfolios is obtained. Advantages of this stochastic method in our context are as follows: (i) Mean value-stochastic goal programming relies on classical utility theory under uncertainty and Arrow's absolute risk aversion, which ensures soundness and strictness, and (ii) the numerical model is easily solved by using available software such as mean-variance software. Numerical results are tabulated and discussed.