Solving stochastic dynamic programs by convex optimization and simulation

Denis Belomestny, Christian Bender, Fabian Dickmann, Nikolaus Schweizer

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

In this chapter we review some recent progress on Monte Carlo methods for a class of stochastic dynamic programming equations, which accommodates optimal stopping problems and time discretization schemes for backward stochastic differential equations with convex generators. We first provide a primal maximization problem and a dual minimization problem, based on which confidence intervals for the value of the dynamic program can be constructed by Monte Carlo simulation. For the computation of the lower confidence bounds we apply martingale basis functions within a least-squares Monte Carlo implementation. For the upper confidence bounds we suggest a multilevel simulation within a nested Monte Carlo approach and, alternatively, a generic sieve optimization approach with a variance penalty term.
Original languageEnglish
Title of host publicationExtraction of Quantifiable Information from Complex Systems
EditorsT.J. Barth, M. Griebel, D.E. Keyes, R.M. Nieminen, D. Roose, T. Schlick
Place of PublicationCham
PublisherSpringer International Publishing AG
Pages1-23
ISBN (Print)9783319081588
DOIs
Publication statusPublished - 30 Sep 2014
Externally publishedYes

Publication series

NameLecture Notes in Computational Science and Engineering
Volume102

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Belomestny, D., Bender, C., Dickmann, F., & Schweizer, N. (2014). Solving stochastic dynamic programs by convex optimization and simulation. In T. J. Barth, M. Griebel, D. E. Keyes, R. M. Nieminen, D. Roose, & T. Schlick (Eds.), Extraction of Quantifiable Information from Complex Systems (pp. 1-23). [Chapter 1] (Lecture Notes in Computational Science and Engineering; Vol. 102). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-08159-5_1