Optimal switching problems under partial information

Li Kai (), Nyström Kaj () and Olofsson Marcus ()
Additional contact information
Li Kai: Department of Mathematics, Uppsala University, 751 06 Uppsala, Sweden
Nyström Kaj: Department of Mathematics, Uppsala University, 751 06 Uppsala, Sweden
Olofsson Marcus: Department of Mathematics, Uppsala University, 751 06 Uppsala, Sweden

Monte Carlo Methods and Applications, 2015, vol. 21, issue 2, 91-120

Abstract: In this paper, we formulate and study an optimal switching problem under partial information. In our model, the agent/manager/investor attempts to maximize the expected reward by switching between different states/investments. However, he is not fully aware of his environment and only an observation process, which contains partial information about the environment/underlying, is accessible. It is based on the partial information carried by this observation process that all decisions must be made. We propose a probabilistic numerical algorithm, based on dynamic programming, regression Monte Carlo methods, and stochastic filtering theory, to compute the value function. In this paper, the approximation of the value function and the corresponding convergence result are obtained when the underlying and observation processes satisfy the linear Kalman–Bucy setting. A numerical example is included to show some specific features of partial information.

Keywords: Optimal switching problem; partial information; diffusion; regression; Monte Carlo method; Euler scheme; stochastic filtering; Kalman–Bucy filter (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/mcma-2014-0013 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:mcmeap:v:21:y:2015:i:2:p:91-120:n:1

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/mcma/html

DOI: 10.1515/mcma-2014-0013

Access Statistics for this article

Monte Carlo Methods and Applications is currently edited by Karl K. Sabelfeld

More articles in Monte Carlo Methods and Applications from De Gruyter
Bibliographic data for series maintained by Peter Golla ().