Artikel

Modeling the optimal combination of proportional and stop-loss reinsurance with dependent claim and stochastic insurance premium

This paper investigates an optimal reinsurance policy using a risk model with dependent claim and insurance premium by assuming that the insurance premium is random. Their dependence structure is modeled using Sarmanov's bivariate exponential distribution and the Farlie-Gumbel-Morgenstern (FGM) copula-based bivariate exponential distribution. The reinsurance premium paid by the insurer to the reinsurer is fixed and is charged by the expected value premium principle (EVPP) and standard deviation premium principle (SDPP). The main objective of this paper is to determine the proportion and retention limit of the optimal combination of proportional and stop-loss reinsurance for the insurer. Specifically, with a constrained reinsurance premium, we use the minimization of the Value-at-Risk (VaR) of the insurer's net cost. When determining the optimal proportion and retention limit, we provide some numerical examples to illustrate the theoretical results. We show that the dependence parameter, the probability of claim occurrence, and the confidence level have effects on the optimal VaR of the insurer's net cost.

Sprache
Englisch

Erschienen in
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 16 ; Year: 2023 ; Issue: 2 ; Pages: 1-20

Klassifikation
Management
Thema
copula
net cost
stochastic insurance premium
value-at-risk

Ereignis
Geistige Schöpfung
(wer)
Sari, Suci Fratma
Hakim, Arief
Magdalena, Ikha
Syuhada, Khreshna
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2023

DOI
doi:10.3390/jrfm16020095
Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

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

  • Artikel

Beteiligte

  • Sari, Suci Fratma
  • Hakim, Arief
  • Magdalena, Ikha
  • Syuhada, Khreshna
  • MDPI

Entstanden

  • 2023

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