[1]黄伯强,李启才.保险公司最优比例再保险和配对交易策略[J].南京师范大学学报(自然科学版),2019,42(04):39-43.[doi:10.3969/j.issn.1001-4616.2019.04.006]
　Huang Boqiang,Li Qicai.Optimal Proportional Reinsurance and Pairs Trading Polices for Insurer[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(04):39-43.[doi:10.3969/j.issn.1001-4616.2019.04.006]
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保险公司最优比例再保险和配对交易策略()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年04期
页码:
39-43
栏目:
·数学与计算机科学·
出版日期:
2019-12-30

文章信息/Info

Title:
Optimal Proportional Reinsurance and Pairs Trading Polices for Insurer
文章编号:
1001-4616(2019)04-0039-05
作者:
黄伯强1; 李启才2
(1.南京师范大学中北学院,江苏 南京 210023)(2.南京师范大学数学科学学院,江苏 南京 210023)
Author(s):
Huang Boqiang1; Li Qicai2
(1.School of Zhongbei,Nanjing Normal University,Nanjing 210023,China)(2.School of Mathematical Sciences,Nanjing Normal University,Nanjing 210023,China)
关键词:
比例再保险; 配对交易; 价差; 最优策略; 随机控制
Keywords:
proportional reinsurance; pairs trading; spread; optimal polices; stochastic control
分类号:
F830.91
DOI:
10.3969/j.issn.1001-4616.2019.04.006
文献标志码:
A
摘要:
考虑保险公司通过比例再保险转移索赔风险和配对交易策略管理财富的优化问题. 利用经典的复合泊松索赔过程描述保险公司的盈余,同时保险公司投资包含一份股票多头和若干份股票空头的配对资产组合,该资产价差服从均值-回复过程.在终端财富期望指数效用最大化的准则下,利用随机控制理论获得最优的比例再保险和投资策略及值函数的解析式.
Abstract:
This paper discusses optimization problem which the insurer transfer the claims risk by proportional reinsurance and manage the wealth by pairs trading. The surplus of claims is modeled by compound Poisson process. And the insurer can invest it’s wealth into pairs portfolio which include a long position on one stock and a short on another stock. The price spread of this pair follows a mean-reverting stochastic process. Under maximizing of expect exponent utility of the terminal wealth,the optimal proportional reinsurance and pairs trading polices and value function are solved by stochastic control theory.

参考文献/References:

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[10]李启才. 两类保险业务时保险公司的调节系数和再保险策略[J]. 南京师大学报(自然科学版),2015,38(4):42-46.
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备注/Memo

备注/Memo:
收稿日期:2019-07-25.
基金项目:国家自然科学基金(11701288)、南京师范大学青蓝工程项目(2016).
通讯联系人:李启才,博士,副教授,研究方向:随机控制理论及其应用. E-mail:17747873@qq.com
更新日期/Last Update: 2019-12-31