Optimal dividend strategies of two collaborating businesses in the diffusion approximation model
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Optimal dividend strategies of two collaborating businesses in the diffusion approximation model. / Gu, Jia Wen; Steffensen, Mogens; Zheng, Harry.
In: Mathematics of Operations Research, Vol. 43, No. 2, 01.05.2018, p. 377-398.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Optimal dividend strategies of two collaborating businesses in the diffusion approximation model
AU - Gu, Jia Wen
AU - Steffensen, Mogens
AU - Zheng, Harry
PY - 2018/5/1
Y1 - 2018/5/1
N2 - In this paper, we consider the optimal dividend payment strategy for an insurance company that has two collaborating business lines. The surpluses of the business lines are modeled by diffusion processes. The collaboration between the two business lines permits that money can be transferred from one line to another with or without proportional transaction costs, while money must be transferred from one line to another to help both business lines keep running before simultaneous ruin of the two lines eventually occurs.
AB - In this paper, we consider the optimal dividend payment strategy for an insurance company that has two collaborating business lines. The surpluses of the business lines are modeled by diffusion processes. The collaboration between the two business lines permits that money can be transferred from one line to another with or without proportional transaction costs, while money must be transferred from one line to another to help both business lines keep running before simultaneous ruin of the two lines eventually occurs.
KW - Collaborating businesses
KW - Diffusion model
KW - Optimal dividends strategy
KW - Stochastic control
UR - http://www.scopus.com/inward/record.url?scp=85047069887&partnerID=8YFLogxK
U2 - 10.1287/moor.2017.0860
DO - 10.1287/moor.2017.0860
M3 - Journal article
AN - SCOPUS:85047069887
VL - 43
SP - 377
EP - 398
JO - Mathematics of Operations Research
JF - Mathematics of Operations Research
SN - 0364-765X
IS - 2
ER -
ID: 201867124