Discrete-type Approximations for Non-Markovian Optimal Stopping Problems: Part II
Sérgio C. Bezerra (),
Alberto Ohashi (),
Francesco Russo () and
Francys Souza ()
Additional contact information
Sérgio C. Bezerra: Universidade Federal da Paraíba, Rua dos Escoteiros
Alberto Ohashi: Universidade de Brasília
Francesco Russo: Unité de Mathématiques Appliquées
Francys Souza: Universidade de Campinas
Methodology and Computing in Applied Probability, 2020, vol. 22, issue 3, 1221-1255
Abstract In this paper, we present a Longstaff-Schwartz-type algorithm for optimal stopping time problems based on the Brownian motion filtration. The algorithm is based on Leão et al. (??2019) and, in contrast to previous works, our methodology applies to optimal stopping problems for fully non-Markovian and non-semimartingale state processes such as functionals of path-dependent stochastic differential equations and fractional Brownian motions. Based on statistical learning theory techniques, we provide overall error estimates in terms of concrete approximation architecture spaces with finite Vapnik-Chervonenkis dimension. Analytical properties of continuation values for path-dependent SDEs and concrete linear architecture approximating spaces are also discussed.
Keywords: Optimal stopping; Stochastic optimal control; Monte Carlo methods; Primary: 93E20; Secondary: 60H30 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s11009-019-09764-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:22:y:2020:i:3:d:10.1007_s11009-019-09764-y
Ordering information: This journal article can be ordered from
Access Statistics for this article
Methodology and Computing in Applied Probability is currently edited by Joseph Glaz
More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().