Robust portfolio choice with CVaR and VaR under distribution and mean return ambiguity

We consider the problem of optimal portfolio choice using the Conditional Value-at-Risk (CVaR) and Value-at-Risk (VaR) measures for a market consisting of n risky assets and a riskless asset and where short positions are allowed. When the distribution of returns of risky assets is unknown but the mean return vector and variance/covariance matrix of the risky assets are fixed, we derive the distributionally robust portfolio rules. Then, we address uncertainty (ambiguity) in the mean return vector in addition to distribution ambiguity, and derive the optimal portfolio rules when the uncertainty in the return vector is modeled via an ellipsoidal uncertainty set. In the presence of a riskless asset, the robust CVaR and VaR measures, coupled with a minimum mean return constraint, yield simple, mean-variance efficient optimal portfolio rules. In a market without the riskless asset, we obtain a closed-form portfolio rule that generalizes earlier results, without a minimum mean return restriction.     

Effect of Solvent Composition on Protonation Constants of Some Glycine Dipeptides

Stoichiometric protonation constants (log₁₀ K ₁ and log₁₀ K ₂) of some aliphatic dipeptides (Gly–Tyr, Gly–Phe, Gly–Val, Gly–Leu, Gly–Thr, Gly–Met and Gly–Pro) were determined potentiometrically in 20, 40, and 60 % (v/v) 1,4-dioxane–water and dimethyl sulfoxide–water mixtures at 25.0 (±0.1) °C with an ionic strength of 0.10 mol·L^?1 sodium chloride. The protonation constants were calculated with the computer program PKAS and selection of the best fit chemical models is based on the statistical parameters. The effects of solvent composition on these protonation constants are discussed to determine the factors which control these processes. It has been observed that, while the correlation between log₁₀ K ₁ and log₁₀ K ₂ with the percentages of dimethyl sulfoxide in the dimethyl sulfoxide–water mixtures are not linear, these values linearly increase as the concentration of 1,4-dioxane increases in the solvent mixtures.     

Bees Algorithm for constrained fuzzy multi-objective two-sided assembly line balancing problem

Bees Algorithm is one of the swarm intelligence based heuristics which tries to model natural behaviour of honey bees in food foraging and used to solve optimization problems. On the other hand, Two-sided Assembly Line Balancing Problem is a generalization of simple Assembly Line Balancing Problem where different assembly tasks are carried out on the same product in parallel at both left and right sides of the line. Two-sided assembly lines are generally employed for the assembly of large-sized products such as buses and trucks. Furthermore, many real life problems contain imprecise objectives and Fuzzy Multi-objective Programming gives an opportunity to handle such situations. In this study, Two-sided Assembly Line Balancing Problem is considered more realistically by employing positional, zoning and synchronous task constraints and by utilizing fuzzy approaches so as to maximize work slackness index and line efficiency, and minimize total balance delay. For solving this problem Bees Algorithm is used as a search mechanism for obtaining good solutions and extensive computational results are presented.     

Strong valid inequalities for a class of concave submodular minimization problems under cardinality constraints

Mathematical Programming - We study the polyhedral convex hull structure of a mixed-integer set which arises in a class of cardinality-constrained concave submodular minimization problems. This...     

Synthesis and characterization of PVA-based binary-gel electrolytes including massive ions

Journal of Solid State Electrochemistry - The motion of the ion pairs under the applied electric field is an important phenomenon in the storage properties of the capacitive devices. In this study,...