Summary: We focus the attention on characterizations of policies maximizing growth rate of expected utility, along with average of the associated certainty equivalent, in risk-sensitive Markov decision chains with finite state and action spaces. In contrast to the existing literature the problem is handled by methods of stochastic dynamic programming on condition that the transition probabilities are replaced by general nonnegative matrices. Using the block-triangular decomposition of a collection of nonnegative matrices we establish necessary and sufficient conditions guaranteeing independence of optimal values on starting state along with partition of the state space into subsets with constant optimal values. Finally, for models with growth rate independent of the starting state we show how the methods work if we minimize growth rate or average of the certainty equivalent.