Risk-Sensitive Stopping Problems for Continuous-Time Markov Chains

In this thesis we consider optimal stopping problems for continuous-time Markov chains, evaluated under a general class of utility functions. Besides the well known dynamic programming approach via HJB equation, these stopping problems are tackled by a discrete-time approach by using iteration type formulas like the reward iteration or the Bellman equation and by establishing a fixed-point equation. Under general utility functions, the structural properties of optimal stopping times are studied.