A multivariate Markov chain stock model

Guglielmo D'Amico and Riccardo De Blasis

Scandinavian Actuarial Journal, 2020, vol. 2020, issue 4, 272-291

Abstract: We propose a dividend stock valuation model where multiple dividend growth series and their dependencies are modelled using a multivariate Markov chain. Our model advances existing Markov chain stock models. First, we determine assumptions that guarantee the finiteness of the price and risk as well as the fulfilment of transversality conditions. Then, we compute the first- and second-order price-dividend ratios by solving corresponding linear systems of equations and show that a different price-dividend ratio is attached to each combination of states of the dividend growth process of each stock. Subsequently, we provide a formula for the computation of the variances and covariances between stocks in a portfolio. Finally, we apply the theoretical model to the dividend series of three US stocks and perform comparisons with existing models. The results could also be applied for actuarial purposes as a general stochastic investment model and for calculating the initial endowment to fund a portfolio of dependent perpetuities.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1080/03461238.2019.1661280 (text/html)
Access to full text is restricted to subscribers.

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:taf:sactxx:v:2020:y:2020:i:4:p:272-291

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/sact20

DOI: 10.1080/03461238.2019.1661280

Access Statistics for this article

Scandinavian Actuarial Journal is currently edited by Boualem Djehiche

More articles in Scandinavian Actuarial Journal from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().