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In this paper, we develop a portfolio optimization method to maximize the performance of a fixed income portfolio. To achieve this aim, we define a two-step optimization problem where we firstly manage the immunization risk and then we maximize the portfolio wealth in a reward-risk framework. In the first optimization step, we create funds of bonds with constant immunization measure over time, and we propose an innovative immunization measure for bond portfolio management that leads to a more flexible immunization approach and a better trade-off between reward and risk. In the second optimization step, maximizing two performance measures on these baskets of bonds we obtain portfolio strategies that consider different investors' profiles. An empirical application to the US fixed income market during 2002-2012 period is provided. Applying the portfolio optimization method to different bond classes, we compare the results with an equity index. This ex-post analysis indicates the benefits of the proposed portfolio strategy in outperforming the benchmark and proves that capital flows to safer markets during crisis periods. [ABSTRACT FROM AUTHOR]

The article proposes two alternative dispersion measures consistent with additive shifts that can be used in investment portfolio selection problems. It examines the stochastic dominance of mean-dispersion approach and the properties of the two dispersion measures and compares these measures with the standard deviation and the conditional value at risk benchmark on the U.S. stock market. An analysis of portfolio diversification and turnover of investment strategies is presented.

In this note, we examine the impact of non linear reward and risk measures on portfolio selection. In particular, we compare the ex-post final wealth sample paths of strategies based on the Sharpe ratio and strategies based on non-linear reward/risk measures. As suggested by the recent literature, we model dependencies with an asymmetric t copula estimated on the innovations of the marginals that follow an ARMA-GARCH type model. Therefore, we first simulate future scenarios, on the basis of which allocation decisions are made, and then we compare the ex-post final wealth obtained with non-linear risk and reward strategies and the wealth obtained with classic portfolio strategies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a methodology to value the portfolio choices based on the prediction of future returns where the dependence structure of joint returns and the behavior of single returns are estimated separately. In particular, we assume the marginals evolve as an ARMA(0,2)-GARCH(0,2) model with stable paretian residuals and the joint distribution of residuals is estimated with an asymmetric t-copula. Then, we compare the ex-post final wealth sample paths of different strategies based on reward/risk ratios. Doing so we examine and discuss the impact of the forecasting method with respect to classic myopic portfolio strategies based on the same reward/risk ratios. [ABSTRACT FROM AUTHOR]

This paper discusses and analyzes risk measure properties in order to understand how a risk measure has to be used to optimize the investor's portfolio choices. In particular, we distinguish between two admissible classes of risk measures proposed in the portfolio literature: safety-risk measures and dispersion measures. We study and describe how the risk could depend on other distributional parameters. Then, we examine and discuss the differences between statistical parametric models and linear fund separation ones. Finally, we propose an empirical comparison among three different portfolio choice models which depend on the mean, on a risk measure, and on a skewness parameter. Thus, we assess and value the impact on the investor's preferences of three different risk measures even considering some derivative assets among the possible choices. [ABSTRACT FROM AUTHOR]