WIAS Preprint No. 1392, (2009)

Regression methods for stochastic control problems and their convergence analysis


  • Belomestny, Denis
  • Kolodko, Anastasia
  • Schoenmakers, John G. M.
    ORCID: 0000-0002-4389-8266

2010 Mathematics Subject Classification

  • 90C40 90-08 91B28


  • optimal control, dynamic programming, regression estimator, Monte Carlo simulation


In this paper we develop several regression algorithms for solving general stochastic optimal control problems via Monte Carlo. This type of algorithms is particulary useful for problems with a high-dimensional state space and complex dependence structure of the underlying Markov process with respect to some control. The main idea behind the algorithms is to simulate a set of trajectories under some reference measure and to use the Bellman principle combined with fast methods for approximating conditional expectations and functional optimization. Theoretical properties of the presented algorithms are investigated and the convergence to the optimal solution is proved under mild assumptions. Finally, we present numerical results for the problem of pricing a high-dimensional Bermudan basket option under transaction costs in a financial market with a large investor.

Appeared in

  • SIAM J. Control Optim., 48 (2010) pp. 3562--3588.

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