  This paper studies dynamic stochastic optimization problems parametrized by a
random variable. Such problems arise in many applications in operations
research and mathematical finance. We give sufficient conditions for the
existence of solutions and the absence of a duality gap. Our proof uses
extended dynamic programming equations, whose validity is established under new
relaxed conditions that generalize certain no-arbitrage conditions from
mathematical finance.
