Optimal stopping in infinite horizon: An eigenfunction expansion approach

We develop an eigenfunction expansion based value iteration algorithm to solve discrete time infinite horizon optimal stopping problems for a rich class of Markov processes that are important in applications. We provide convergence analysis for the value function and the exercise boundary, and derive easily computable error bounds for value iterations. As an application we develop a fast and accurate algorithm for pricing callable perpetual bonds under the CIR short rate model.