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A pair of optimal reinsurance–investment strategies in the two-sided exit framework

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  • Landriault, David
  • Li, Bin
  • Li, Danping
  • Li, Dongchen

Abstract

In this paper, we derive and study a pair of optimal reinsurance–investment strategies under the two-sided exit framework which aims to (1) maximize the probability that the surplus reaches the target b before ruin occurs over the time horizon [0,eλ] (where eλ is an independent exponentially distributed random time); (2) minimize the probability that ruin occurs before the surplus reaches the target b over the time horizon [0,eλ]. We assume the insurer can purchase proportional reinsurance and invest its wealth in a financial market consisting of a risk-free asset and a risky asset, where the dynamics of the latter is assumed to be correlated with the insurance surplus. By solving the associated Hamilton–Jacobi–Bellman (HJB) equation via a dual argument, an explicit expression for the optimal reinsurance–investment strategy is obtained. We find that the optimal strategy of objective (1) (objective (2) resp.) is always more aggressive (conservative resp.) than the strategy of minimizing the infinite-time ruin probability of Promislow and Young (2005). Due to the presence of the time factor eλ, the optimal strategy under objective (1) or (2) may lead to more aggressive positions as the wealth level increases, a behavior which may be more consistent with industry practices.

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  • Landriault, David & Li, Bin & Li, Danping & Li, Dongchen, 2016. "A pair of optimal reinsurance–investment strategies in the two-sided exit framework," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 284-294.
    • Handle: RePEc:eee:insuma:v:71:y:2016:i:c:p:284-294
    DOI: 10.1016/j.insmatheco.2016.09.002
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    References listed on IDEAS

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    1. Bayraktar, Erhan & Promislow, S. David & Young, Virginia R., 2014. "Purchasing life insurance to reach a bequest goal," Insurance: Mathematics and Economics, Elsevier, vol. 58(C), pages 204-216.
    2. Bayraktar, Erhan & Young, Virginia R., 2016. "Optimally investing to reach a bequest goal," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 1-10.
    3. Junna Bi & Qingbin Meng & Yongji Zhang, 2014. "Dynamic mean-variance and optimal reinsurance problems under the no-bankruptcy constraint for an insurer," Annals of Operations Research, Springer, vol. 212(1), pages 43-59, January.
    4. Virginia Young, 2004. "Optimal Investment Strategy to Minimize the Probability of Lifetime Ruin," North American Actuarial Journal, Taylor & Francis Journals, vol. 8(4), pages 106-126.
    5. Chen, Shumin & Li, Zhongfei & Li, Kemian, 2010. "Optimal investment-reinsurance policy for an insurance company with VaR constraint," Insurance: Mathematics and Economics, Elsevier, vol. 47(2), pages 144-153, October.
    6. Cai, Jun & Tan, Ken Seng & Weng, Chengguo & Zhang, Yi, 2008. "Optimal reinsurance under VaR and CTE risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 43(1), pages 185-196, August.
    7. Erhan Bayraktar & S. David Promislow & Virginia R. Young, 2015. "Purchasing Term Life Insurance to Reach a Bequest Goal: Time-Dependent Case," North American Actuarial Journal, Taylor & Francis Journals, vol. 19(3), pages 224-236, July.
    8. Lihua Bai & Huayue Zhang, 2008. "Dynamic mean-variance problem with constrained risk control for the insurers," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 68(1), pages 181-205, August.
    9. S. David Promislow & Virginia Young, 2005. "Minimizing the Probability of Ruin When Claims Follow Brownian Motion with Drift," North American Actuarial Journal, Taylor & Francis Journals, vol. 9(3), pages 110-128.
    10. Sid Browne, 1999. "Beating a moving target: Optimal portfolio strategies for outperforming a stochastic benchmark," Finance and Stochastics, Springer, vol. 3(3), pages 275-294.
    11. Yuping Liu & Jin Ma, 2009. "Optimal reinsurance/investment problems for general insurance models," Papers 0908.4538, arXiv.org.
    12. Victor C. Pestien & William D. Sudderth, 1985. "Continuous-Time Red and Black: How to Control a Diffusion to a Goal," Mathematics of Operations Research, INFORMS, vol. 10(4), pages 599-611, November.
    13. Sid Browne, 1997. "Survival and Growth with a Liability: Optimal Portfolio Strategies in Continuous Time," Mathematics of Operations Research, INFORMS, vol. 22(2), pages 468-493, May.
    14. Ronnie Loeffen & Irmina Czarna & Zbigniew Palmowski, 2011. "Parisian ruin probability for spectrally negative L\'{e}vy processes," Papers 1102.4055, arXiv.org, revised Mar 2013.
    15. Luo, Shangzhen & Taksar, Michael & Tsoi, Allanus, 2008. "On reinsurance and investment for large insurance portfolios," Insurance: Mathematics and Economics, Elsevier, vol. 42(1), pages 434-444, February.
    16. Liang, Zhibin & Bayraktar, Erhan, 2014. "Optimal reinsurance and investment with unobservable claim size and intensity," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 156-166.
    17. Bai, Lihua & Guo, Junyi, 2008. "Optimal proportional reinsurance and investment with multiple risky assets and no-shorting constraint," Insurance: Mathematics and Economics, Elsevier, vol. 42(3), pages 968-975, June.
    18. Zeng, Yan & Li, Danping & Gu, Ailing, 2016. "Robust equilibrium reinsurance-investment strategy for a mean–variance insurer in a model with jumps," Insurance: Mathematics and Economics, Elsevier, vol. 66(C), pages 138-152.
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