Approximating the Solution of Stochastic Optimal Control Problems and the Merton’s Portfolio Selection Model

Behzad Kafash ()
Additional contact information
Behzad Kafash: Ardakan University

Computational Economics, 2019, vol. 54, issue 2, No 12, 763-782

Abstract: Abstract In this paper, a numerical algorithm is presented to solve stochastic optimal control problems via the Markov chain approximation method. This process is based on state and time spaces discretization followed by a backward iteration technique. First, the original controlled process by an appropriate controlled Markov chain is approximated. Then, the cost functional is appropriate for the approximated Markov chain. Also, the finite difference approximations are used to the construction of locally consistent approximated Markov chain. Furthermore, the coefficients of the resulting discrete equation can be considered as the desired transition probabilities and interpolation interval. Finally, the performance of the presented algorithm on a test case with a well-known explicit solution, namely the Merton’s portfolio selection model, is demonstrated.

Keywords: Stochastic optimal control problems; Markov chain approximation; Dynamic programming; Hamilton–Jacobi–Bellman (HJB)equations; Merton’s portfolio selection model (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-018-9852-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:kap:compec:v:54:y:2019:i:2:d:10.1007_s10614-018-9852-3

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-018-9852-3

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().