Generalized bounds for convex multistage stochastic programs This book investigates convex multistage stochastic programs whose objective and constraint functions exhibit a generalized nonconvex dependence on the random parameters. Although the classical Jensen and Edmundson Madansky type bounds or its extensions are generally not available for such problems tight bounds can systematically be constructed under mild regularity conditions. A nice primal dual symmetry property is revealed when the proposed bounding method is applied to linear stochastic programs. After having developed the theoretical concepts exemplary real life applications are studied. It is shown how market power lognormal stochastic processes and risk aversion can be properly handled in a stochastic programming framework. Numerical experiments show that the relative gap between the bounds can be reduced to a few percent without exploding the problem size