portfolio optimization

This study develops a scenario-based optimization model for state governance of resource provision for military operations under wartime conditions, explicitly accounting for high external turbulence, binding budget constraints, time pressure, and the risk of failing critical provision standards.  The study proposes a scenario-based and probability-driven analytical framework that ranks governance initiatives by expected effect and risk and selects an optimal portfolio of initiatives under given budget and risk constraints.  Methodologically, external turbulence is mode

Risk aversion plays a significant and central role in investors’ decisions in the process of developing a portfolio.  In this portfolio optimization framework, we determine the portfolio that possesses the minimal risk by using a new geometrical method.  For this purpose, we elaborate an algorithm that enables us to compute any Euclidean distance to a standard simplex.  With this new approach, we can treat the case of portfolio optimization without short-selling in its entirety, and we also recover in geometrical terms the well-known results on portfolio optimization wi