We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model …

requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a … predicting financial risk, which is essential for both financial companies and their customers. In addition, results show that …

benchmark for all decreasing absolute risk-averse investors, using Quadratic Programming. The method is applied to standard data … the performance of Mean-Variance optimization by tens to hundreds of basis points per annum, for low to medium risk levels …. The improvements critically depend on imposing the complex condition of Decreasing Absolute Risk Aversion in addition to …

In this paper, we focus on the portfolio optimization problem associated to a quasiconvex risk measure (satisfying some … additional assumptions). For coherent/convex risk measures, the portfolio optimization problem has been already studied by … characterize optimal solutions of the portfolio problem associated to quasiconvex risk measures. The shape of the efficient …

The theory of convex risk functions has now been well established as the basis for identifying the families of risk … functions that should be used in risk-averse optimization problems. Despite its theoretical appeal, the implementation of a … convex risk function remains difficult, as there is little guidance regarding how a convex risk function should be chosen so …

expansion project. Optimization is studied by methods of stochastic control theory. Numerical algorithms are presented which …