File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Approximation of optimal ergodic dividend strategies using controlled Markov chains

TitleApproximation of optimal ergodic dividend strategies using controlled Markov chains
Authors
Keywordsapproximation theory
dynamic programming
optimal control
Markov processes
discrete time systems
Issue Date2018
PublisherThe Institution of Engineering and Technology. The Journal's web site is located at http://www.ietdl.org/IP-CTA
Citation
IET Control Theory and Applications, 2018, v. 12 n. 16, p. 2194-2204 How to Cite?
AbstractThis study develops a numerical method to find optimal ergodic (long-run average) dividend strategies in a regime-switching model. The surplus process is modelled by a regime-switching process subject to liability constraints. The regime-switching process is modelled by a finite-time continuous-time Markov chain. Using the dynamic programming principle, the optimal long-term average dividend payment is a solution to the coupled system of Hamilton–Jacobi–Bellman equations. Under suitable conditions, the optimal value of the long-term average dividend payment can be determined by using an invariant measure. However, due to the regime switching, getting the invariant measure is very difficult. The objective is to design a numerical algorithm to approximate the optimal ergodic dividend payment strategy. By using the Markov chain approximation techniques, the authors construct a discrete-time controlled Markov chain for the approximation, and prove the convergence of the approximating sequences. A numerical example is presented to demonstrate the applicability of the algorithm.
Persistent Identifierhttp://hdl.handle.net/10722/272971
ISSN
2017 Impact Factor: 3.296
2015 SCImago Journal Rankings: 1.467

 

DC FieldValueLanguage
dc.contributor.authorJin, Z-
dc.contributor.authorYang, H-
dc.contributor.authorYin, G-
dc.date.accessioned2019-08-06T09:20:08Z-
dc.date.available2019-08-06T09:20:08Z-
dc.date.issued2018-
dc.identifier.citationIET Control Theory and Applications, 2018, v. 12 n. 16, p. 2194-2204-
dc.identifier.issn1751-8644-
dc.identifier.urihttp://hdl.handle.net/10722/272971-
dc.description.abstractThis study develops a numerical method to find optimal ergodic (long-run average) dividend strategies in a regime-switching model. The surplus process is modelled by a regime-switching process subject to liability constraints. The regime-switching process is modelled by a finite-time continuous-time Markov chain. Using the dynamic programming principle, the optimal long-term average dividend payment is a solution to the coupled system of Hamilton–Jacobi–Bellman equations. Under suitable conditions, the optimal value of the long-term average dividend payment can be determined by using an invariant measure. However, due to the regime switching, getting the invariant measure is very difficult. The objective is to design a numerical algorithm to approximate the optimal ergodic dividend payment strategy. By using the Markov chain approximation techniques, the authors construct a discrete-time controlled Markov chain for the approximation, and prove the convergence of the approximating sequences. A numerical example is presented to demonstrate the applicability of the algorithm.-
dc.languageeng-
dc.publisherThe Institution of Engineering and Technology. The Journal's web site is located at http://www.ietdl.org/IP-CTA-
dc.relation.ispartofIET Control Theory and Applications-
dc.rightsThis paper is a postprint of a paper submitted to and accepted for publication in IET Control Theory and Applications and is subject to IET copyright. The copy of record is available at IET Digital Library [DOI: 10.1049/iet-cta.2018.5394]-
dc.subjectapproximation theory-
dc.subjectdynamic programming-
dc.subjectoptimal control-
dc.subjectMarkov processes-
dc.subjectdiscrete time systems-
dc.titleApproximation of optimal ergodic dividend strategies using controlled Markov chains-
dc.typeArticle-
dc.identifier.emailYang, H: hlyang@hku.hk-
dc.identifier.authorityYang, H=rp00826-
dc.description.naturepostprint-
dc.identifier.doi10.1049/iet-cta.2018.5394-
dc.identifier.scopuseid_2-s2.0-85055288828-
dc.identifier.hkuros299914-
dc.identifier.volume12-
dc.identifier.issue16-
dc.identifier.spage2194-
dc.identifier.epage2204-
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats