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arXiv · 1808.04725

Dynamic programming for optimal stopping via pseudo-regression

Christian Bayer·Martin Redmann·John Schoenmakers
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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 $L^2$ 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 less computational cost for a pre-specified error tolerance, hence to lower complexity. The method is justified by numerical examples.

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Christian Bayer, Martin Redmann, John Schoenmakers. 2018-08-10. Dynamic programming for optimal stopping via pseudo-regression. https://arxiv.org/abs/1808.04725

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