Optimal stopping in infinite horizon: An eigenfunction expansion approach

Lingfei Li*, Vadim Linetsky

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)122-128
Number of pages7
JournalStatistics and Probability Letters
Issue number1
StatePublished - Feb 2014


  • Callable perpetual bonds
  • Eigenfunction expansions
  • Optimal stopping
  • Symmetric Hunt processes
  • Value iterations

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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