Dynamic programming for optimal stopping via pseudo-regression

Christian Bayer, Martin Redmann and John Schoenmakers

Quantitative Finance, 2021, vol. 21, issue 1, 29-44

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.

Date: 2021
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DOI: 10.1080/14697688.2020.1780299

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