Insurance risk exchange in the presence of background risk and private information A continuous-time model

被引:1
|
作者
Lin, Wen-chang [1 ]
Lu, Jin-ray [2 ]
机构
[1] Natl Chung Cheng Univ, Dept Finance, Chiayi, Taiwan
[2] Natl Dong Hwa Univ, Dept Finance, Hualien, Taiwan
关键词
Insurance; Risk analysis; Information exchange;
D O I
10.1108/15265940710750512
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Purpose - The purpose of this study is to introduce an insurance risk-exchange model in the presence of background risk and private information and which solves the optimal insurance and investment decisions simultaneously. Design/methodology/approach - The model undertakes a continuous-time two-agent framework in which the decisions depend on who can determine the insurance quantity as well as the agents' risk attitudes. The decisions are solved by using dynamic-programming techniques. Findings - The results show that the insured may purchase full insurance even if the insurance price is actuarially unfair and the insurance risk and investment risk are uncorrelated. Further, the demand for insurance may be affected by background risk even if the two risks are uncorrelated. If Pareto optimality is impeded by private information, the paper shows that the deadweight loss can be mitigated by forming a hedging demand with respect to the parameter risk. Originality/value - This study is not only an extension of the existing continuous-time insurance demand model, but also may be considered a model of "enterprise risk management" for institutional agents.
引用
收藏
页码:288 / 308
页数:21
相关论文
共 50 条
  • [21] On the expected discounted penalty function for the continuous-time compound binomial risk model
    Liu, Guoxin
    Wang, Ying
    STATISTICS & PROBABILITY LETTERS, 2008, 78 (15) : 2446 - 2455
  • [22] Continuous-time mean-risk portfolio selection
    Jin, HQ
    Yan, HA
    Zhou, XY
    ANNALES DE L INSTITUT HENRI POINCARE-PROBABILITES ET STATISTIQUES, 2005, 41 (03): : 559 - 580
  • [23] Assessing the solvency of insurance portfolios via a continuous-time cohort model
    Jevtic, Petar
    Regis, Luca
    INSURANCE MATHEMATICS & ECONOMICS, 2015, 61 : 36 - 47
  • [24] Which continuous-time model is most appropriate for exchange rates?
    Erdemlioglu, Deniz
    Laurent, Sebastien
    Neely, Christopher J.
    JOURNAL OF BANKING & FINANCE, 2015, 61 : S256 - S268
  • [25] A CONTINUOUS-TIME STOCHASTIC MODEL OF CELL MOTION IN THE PRESENCE OF A CHEMOATTRACTANT
    Dallon, J. C.
    Despain, Lynnae C.
    Evans, Emily J.
    Grant, Christopher P.
    DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B, 2020, 25 (12): : 4839 - 4852
  • [26] Risk measurement in the presence of background risk
    Tsanakas, Andreas
    INSURANCE MATHEMATICS & ECONOMICS, 2008, 42 (02): : 520 - 528
  • [27] Ruin Probabilities of Continuous-Time Risk Model with Dependent Claim Sizes and Interarrival Times
    Nguyen Huy Hoang
    Bao Quoc Ta
    INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2020, 28 (28) : 69 - 80
  • [28] Time-Coherent Risk Measures for Continuous-Time Markov Chains
    Dentcheva, Darinka
    Ruszczynski, Andrzej
    SIAM JOURNAL ON FINANCIAL MATHEMATICS, 2018, 9 (02): : 690 - 715
  • [29] Comparing the reliability of a discrete-time and a continuous-time Markov chain model in determining credit risk
    Lu, Su-Lien
    APPLIED ECONOMICS LETTERS, 2009, 16 (11) : 1143 - 1148
  • [30] Dynamic mean-risk portfolio selection with multiple risk measures in continuous-time
    Gao, Jianjun
    Xiong, Yan
    Li, Duan
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2016, 249 (02) : 647 - 656