Optimal decisions robust to future uncertainties are considered. Both continuous and discrete sets of scenarios are discussed with algorithms for solving both cases. In the case of the former a quasi-Newton algorithm is discussed and in the case of the latter, a fast and easily implementable...

The General Linear Model (GLM) is the parent model of econometrics. Simultaneous equations and seemingly unrelated regression equation (SURE) models, to name but a few, can be formulated as a GLM. The estimation of the GLM can be viewed as a Generalized Linear Least-Squares problem (GLLSP). The...

In this paper we discuss a basic framework for a grid computing market. It has long been argued that pricing of computer resources can act as a scheduling protocol. We take this idea to its natural conclusion by discussing the basic properties of such a model. We introduce agents that own...

In this paper we consider expected value and mean variance optimization of a general forward--looking stochastic model. The problem is transformed into a general--nonlinear programming problem by adding extra constraints, which restrict the policy maker to commit to a certain policy. Based on...

We present a continuous minimax model for robust portfolio optimization based on worst-case analysis. The classical Markowitz framework is extended to continuous minimax with upper and lower bounds on the return scenarios and a discrete number of rival risk scenarios. The model integrates...

The inability of standard intertemporal economic models under resonable values of the key parameters to generate the observed equity premium is termed as the equity premium puzzle. In order to resolve the puzzle, we propose a new approach based on worst-case analysis where agents make decisions...