Arbeitspapier

Optimal dividend payout under stochastic discounting

Adopting a probabilistic approach we determine the optimal dividend payout policy of a firm whose surplus process follows a controlled arithmetic Brownian motion and whose cash flows are discounted at a stochastic dynamic rate. Dividends can be paid to shareholders at unrestricted rates so that the problem is cast as one of singular stochastic control. The stochastic interest rate is modelled by a Cox-Ingersoll-Ross (CIR) process and the firm's objective is to maximize the total expected ow of discounted dividends until a possible insolvency time. We find an optimal dividend payout policy which is such that the surplus process is kept below an endogenously determined stochastic threshold expressed as a decreasing function r ↦ b(r) of the current interest rate value. We also prove that the value function of the singular control problem solves a variational inequality associated to a second-order, non-degenerate elliptic operator, with a gradient constraint.

Sprache
Englisch

Erschienen in
Series: Center for Mathematical Economics Working Papers ; No. 636

Klassifikation
Wirtschaft
Portfolio Choice; Investment Decisions
Thema
Optimal dividend
stochastic interest rates
CIR model
singular control
optimal stopping
free boundary problems

Ereignis
Geistige Schöpfung
(wer)
Bandini, Elena
De Angelis, Tiziano
Ferrari, Giorgio
Gozzi, Fausto
Ereignis
Veröffentlichung
(wer)
Bielefeld University, Center for Mathematical Economics (IMW)
(wo)
Bielefeld
(wann)
2020

Handle
URN
urn:nbn:de:0070-pub-29436842
Letzte Aktualisierung
20.09.2024, 08:25 MESZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Bandini, Elena
  • De Angelis, Tiziano
  • Ferrari, Giorgio
  • Gozzi, Fausto
  • Bielefeld University, Center for Mathematical Economics (IMW)

Entstanden

  • 2020

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