  We propose a risk measurement approach for a risk-averse stochastic problem.
We provide results that guarantee that our problem has a solution. We
characterize and explore the properties of the argmin as a risk measure and the
minimum as a deviation measure. We provide a connection between linear
regression models and our framework. Based on this conception, we consider
conditional risk and provide a connection between the minimum deviation
portfolio and linear regression. Moreover, we also link the optimal replication
hedging to our framework.
