Portfolio optimization with two quasiconvex risk measures

Portfolio Optimization with Quasiconvex Risk Measures

Portfolio Optimization with Higher Moment Risk Measures

The paper considers modeling of risk-averse preferences in stochastic programming problems using risk measures. We utilize the axiomatic foundation of coherent risk measures and deviation measures in order to develop simple representations that express risk measures via solutions of specially constructed stochastic programming problems. Using the developed representations, we introduce a new fa...

Portfolio optimization with quantile-based risk measures

In this thesis we analyze Portfolio Optimization risk-reward theory, a generalization of the mean-variance theory, in the cases where the risk measures are quantile-based (such as the Value at Risk (V aR) and the shortfall). We show, using multicriteria theory arguments, that if the measure of risk is convex and the measure of reward concave with respect to the allocation vector, then the expec...

Portfolio Optimization with Spectral Measures of Risk

We study Spectral Measures of Risk from the perspective of portfolio optimization. We derive exact results which extend to general Spectral MeasuresMφ the Pflug—Rockafellar—Uryasev methodology for the minimization of α—Expected Shortfall. The minimization problem of a spectral measure is shown to be equivalent to the minimization of a suitable function which contains additional parameters, but ...

Using MODEA and MODM with Different Risk Measures for Portfolio Optimization

The purpose of this study is to develop portfolio optimization and assets allocation using our proposed models. The study is based on a non-parametric efficiency analysis tool, namely Data Envelopment Analysis (DEA). Conventional DEA models assume non-negative data for inputs and outputs. However, many of these data take the negative value, therefore we propose the MeanSharp-βRisk (MShβR) model...