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Optimal stopping via reinforced regression

Author

Listed:
  • Denis Belomestny
  • John Schoenmakers
  • Vladimir Spokoiny
  • Bakhyt Zharkynbay

Abstract

In this note we propose a new approach towards solving numerically optimal stopping problems via reinforced regression based Monte Carlo algorithms. The main idea of the method is to reinforce standard linear regression algorithms in each backward induction step by adding new basis functions based on previously estimated continuation values. The proposed methodology is illustrated by a numerical example from mathematical finance.

Suggested Citation

  • Denis Belomestny & John Schoenmakers & Vladimir Spokoiny & Bakhyt Zharkynbay, 2018. "Optimal stopping via reinforced regression," Papers 1808.02341, arXiv.org, revised Jul 2019.
    • Handle: RePEc:arx:papers:1808.02341
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    References listed on IDEAS

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    1. Carriere, Jacques F., 1996. "Valuation of the early-exercise price for options using simulations and nonparametric regression," Insurance: Mathematics and Economics, Elsevier, vol. 19(1), pages 19-30, December.
    2. Broadie, Mark & Glasserman, Paul, 1997. "Pricing American-style securities using simulation," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1323-1352, June.
    3. Denis Belomestny, 2011. "Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates," Finance and Stochastics, Springer, vol. 15(4), pages 655-683, December.
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