Regression Methods for Markovian Control Problems

Denis Belomestny () and John Schoenmakers
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Denis Belomestny: Universität Duisburg-Essen
John Schoenmakers: The Weierstrass Institute

Chapter Chapter 8 in Advanced Simulation-Based Methods for Optimal Stopping and Control, 2018, pp 135-158 from Palgrave Macmillan

Abstract: Abstract In this chapter we deal with several regression based algorithms for solving the Markovian optimal control problem (5.4) via Monte Carlo. The main idea is to simulate a set of trajectories under some reference measure and then apply a dynamic programming formulation (Bellman principle) to compute recursively estimates for the optimal control process and the optimal stopping rule, where the fast approximation methods allow for computing conditional expectations without nested simulations.

Keywords: Fast Approximation Method; Optimal Stopping Problem; Local Regression Estimators; Pricing Bermudan Options; Global Regression Methods (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:pal:palchp:978-1-137-03351-2_8

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DOI: 10.1057/978-1-137-03351-2_8

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