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Dynamic programming for optimal stopping via pseudo-regression

Author

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  • Christian Bayer
  • Martin Redmann
  • John Schoenmakers

Abstract

We introduce new variants of classical regression-based algorithms for optimal stopping problems based on computation of regression coefficients by Monte Carlo approximation of the corresponding $L^{2} $L2 inner products instead of the least-squares error functional. Coupled with new proposals for simulation of the underlying samples, we call the approach “pseudo regression”. A detailed convergence analysis is provided and it is shown that the approach asymptotically leads to lower computational cost for a pre-specified error tolerance, hence to lower complexity. The method is justified by numerical examples.

Suggested Citation

  • Christian Bayer & Martin Redmann & John Schoenmakers, 2021. "Dynamic programming for optimal stopping via pseudo-regression," Quantitative Finance, Taylor & Francis Journals, vol. 21(1), pages 29-44, January.
    • Handle: RePEc:taf:quantf:v:21:y:2021:i:1:p:29-44
    DOI: 10.1080/14697688.2020.1780299
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