Open-loop equilibrium mean-variance reinsurance, new business and investment strategies with constraints

Liming Zhang; Rongming Wang; Jiaqin Wei

doi:10.3934/jimo.2021140

Article Contents

2022, Volume 18, Issue 6: 3897-3927. Doi: 10.3934/jimo.2021140

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Open-loop equilibrium mean-variance reinsurance, new business and investment strategies with constraints

1.
School of Statistics, East China Normal University, Shanghai 200062, China
2.
Key Laboratory of Advanced Theory and, Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai 200241, China

^* Corresponding author: jqwei@stat.ecnu.edu.cn
^* Corresponding author: jqwei@stat.ecnu.edu.cn

Received: March 2021

Revised: May 2021

Early access: August 2021

Published: September 2022

This work was supported by the Central Universities (2020ECNU-HWFW003), the 111 Project (B14019); the National Natural Science Foundation of China (12071147, 12071146, 11571113, 11601157, 11601320); the China Scholarship Council (201906140044); Graduate Research Innovation Program of School of Statistics, East China Normal University

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Abstract

In this paper, we study a general mean-variance reinsurance, new business and investment problem, where the claim processes of original and new businesses are modeled by two different risk processes and the safety loadings of reinsurance and new business are different. The retention level of the insurer is constrained in $ [0,1] $ and the controls of new business and risky investment are required to be non-negative. This model relaxes the limitations of those in existing research. By using the projection onto the convex set controls valued in, we obtain an open-loop equilibrium reinsurance-new business-investment strategy explicitly. We also show that the obtained equilibrium strategy is the optimal one among all deterministic strategies in the sense that it yields the smallest mean-variance cost. In the case where original and new businesses are the same, the equilibrium strategy is given in closed-form and its sensitivities to safety loadings are shown by numerical examples. At last, by comparing with the case where acquiring new business is prohibited, we show that allowing writing new policies indeed improves the performance of the insurer's risk management.

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Mathematics Subject Classification: Primary: 90B50, 93E20; Secondary: 91G80.

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Figure 1. Equilibrium strategies, $a_{1} = 0.2, \ b_{1} = 0.4, \ a_{2} = 0.3, \ b_{21} = 0, \ b_{22} = 0.5, \ r = 0.05, \ \mu = 0.2, \ \sigma_{1} = \sigma_{2} = 0, \ \sigma_{3} = 0.3, \ T = 10, \ \lambda = 1 $

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Figure 2. Equilibrium retention level and new business strategy, $a_{1} = a_{2} = 0.2, \ b_{1} = b_{21} = 0.4, \ b_{22} = 0, \ r = 0.05, \ \mu = 0.2, \ \sigma_{2} = 0, \ \sigma_{3} = 0.3, \ T = 10, \ \lambda = 1 $

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Figure 3. Equilibrium investment strategy, $a_{1} = a_{2} = 0.2, \ b_{1} = b_{21} = 0.4, \ b_{22} = 0, \ r = 0.05, \ \mu = 0.2, \ \sigma_{2} = 0, \ \sigma_{3} = 0.3, \ T = 10, \ \lambda = 1$

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Table 1. Relations among $ x_{i} $, $ i = 1,2,3,4 $

Cases	Relations
$ \sigma_{1}>\frac{\lambda\left(\mu-r\right)}{b_{1}P_{0}} $	$ 0 <x_{1}<x_{3}<x_{2}<x_{4} $
$ \sigma_{1}=\frac{\lambda\left(\mu-r\right)}{b_{1}P_{0}} $	$ 0<x_{1}<x_{3}=x_{2}=x_{4} $
$ \sigma_{1}\in\left(0,\frac{\lambda\left(\mu-r\right)}{b_{1}P_{0}}\right) $	$ 0<x_{1}<x_{4}<x_{2}<x_{3} $
$ \sigma_=0$	$ 0=x_{1}<x_{2}=x_{4} $
$ \sigma_{1}\in\left(-\frac{\sigma_{3}^{2}b_{1}P_{0}}{\lambda\left(\mu-r\right)},0\right) $	$ x_{3}<x_{1}<0<x_{2}<x_{4} $
$ \sigma_{1}\leq-\frac{\sigma_{3}^{2}b_{1}P_{0}}{\lambda\left(\mu-r\right)} $	$ x_{3}<x_{1}<x_{2}\leq0<x_{4} $

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Table 2. Comparisons of $ \widehat{\boldsymbol{u}}_{1}$ and $\boldsymbol{u}_{1}^{*}$

Cases		$p^{\ast}- \widehat{p}$	$\pi^{\ast}- \widehat{\pi}$
	$\eta\leq x_{1}$	0	$\frac{\lambda a_{1}\sigma_{1}\left(x_{1}-\eta\right)}{\sigma_{3}^{2}b_{1}P_{0}}\geq0$
$\sigma_{1}\geq\frac{\lambda\left(\mu-r\right)}{b_{1}P_{0}}$	$\eta\in\left(x_{1}, x_{2}\right)$	0	0
	$\eta\in\left[x_{3}, x_{2}\right)$	$\frac{\lambda a_{1}\sigma_{1}^{2}\left(\eta-x_{3}\right)}{\sigma_{3}^{2}b_{1}^{2}P_{0}}\geq0$	0
	$\eta\in\left[x_{2}, x_{4}\right)$	$\frac{\lambda a_{1}\left(x_{4}-\eta\right)}{b_{1}^{2} P_{0}}>0$	0
	$\eta\geq x_{4}$	0	0
	$\eta\leq x_{1}$	0	$\frac{\lambda a_{1}\sigma_{1}\left(\sigma_{1}^{2}+\sigma_{3}^{2}\right)\left(x_{1}-\eta\right)}{\left(\sigma_{1}^{2}+\sigma_{3}^{2}\right)\sigma_{3}^{2}b_{1}P_{0}}\geq0$
$\sigma_{1}\in\left(0, \frac{\lambda\left(\mu-r\right)}{b_{1}P_{0}}\right)$	$\eta\in\left(x_{1}, x_{2}\right)$	0	0
	$\eta\in\left[x_{2}, x_{3}\right)$	0	$\frac{\lambda a_{1}\sigma_{1}\left(x_{2}-\eta\right)}{\sigma_{3}^{2}b_{1}P_{0}}\leq0$
	$\eta\geq x_{3}$	0	$\frac{\sigma_{1}b_{1}P_{0}-\lambda\left(\mu-r\right)}{\left(\sigma_{1}^{2}+\sigma_{3}^{2}\right)P_{0}} <0$
$\sigma_{1}\leq0$	$\eta<x_{2}$	0	0
	$\eta\geq x_{2}$	0	$\frac{\lambda a_{1}\sigma_{1}\left(x_{2}-\eta\right)}{\sigma_{3}^{2}b_{1}P_{0}}\leq0$

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References

[1]	S. Asmussen, B. J. Christensen and M. Taksar, Portfolio size as function of the premium: Modelling and optimization, Stochastics, 85 (2010), 575-588. doi: 10.1080/17442508.2013.797426.
[2]	L. Bai and J. Guo, Optimal proportional reinsurance and investment with multiple risky assets and no-shorting constraint, Insurance Math. Econom., 42 (2008), 968-975. doi: 10.1016/j.insmatheco.2007.11.002.
[3]	L. Bai and H. Zhang, Dynamic mean-variance problem with constrained risk control for the insurers, Math. Methods Oper. Res., 68 (2008), 181-205. doi: 10.1007/s00186-007-0195-4.
[4]	N. Bäuerle, Benchmark and mean-variance problems for insurers, Math. Methods Oper. Res., 62 (2005), 159-165. doi: 10.1007/s00186-005-0446-1.
[5]	J. Bi and J. Cai, Optimal investment–reinsurance strategies with state dependent risk aversion and VaR constraints in correlated markets, Insurance Math. Econom., 85 (2019), 1-14. doi: 10.1016/j.insmatheco.2018.11.007.
[6]	J. Bi, Z. Liang and F. Xu, Optimal mean–variance investment and reinsurance problems for the risk model with common shock dependence, Insurance Math. Econom., 70 (2016), 245-258. doi: 10.1016/j.insmatheco.2016.06.012.
[7]	T. Björk, M. Khapko and A. Murgoci, On time-inconsistent stochastic control in continuous time, Finance Stoch., 21 (2017), 331-360. doi: 10.1007/s00780-017-0327-5.
[8]	T. Björk and A. Murgoci, A general theory of Markovian time inconsistent stochastic control problems, Preprint SSRN, 55 (2010).
[9]	T. Björk, A. Murgoci and X. Zhou, Mean–variance portfolio optimization with state-dependent risk aversion, Math. Finance, 24 (2014), 1-24. doi: 10.1111/j.1467-9965.2011.00515.x.
[10]	S. Browne, Optimal investment policies for a firm with a random risk process: Exponential utility and minimizing the probability of ruin, Math. Oper. Res., 20 (1995), 937-958. doi: 10.1287/moor.20.4.937.
[11]	L. Chen, L. Qian, Y. Shen and W. Wang, Constrained investment–reinsurance optimization with regime switching under variance premium principle, Insurance Math. Econom., 71 (2016), 253-267. doi: 10.1016/j.insmatheco.2016.09.009.
[12]	P. Chen and S. C. P. Yam, Optimal proportional reinsurance and investment with regime-switching for mean-variance insurers, Insurance Math. Econom., 53 (2013), 871-883. doi: 10.1016/j.insmatheco.2013.10.004.
[13]	S. Chen, Z. Li and K. Li, Optimal investment-reinsurance policy for an insurance company with VaR constraint, Insurance Math. Econom., 47 (2013), 144-153. doi: 10.1016/j.insmatheco.2010.06.002.
[14]	I. Ekeland and T. A. Pirvu, Investment and consumption without commitment, Math. Financ. Econ., 2 (2008), 57-86. doi: 10.1007/s11579-008-0014-6.
[15]	N. El Karoui, S. Peng and M. C. Quenez, Backward stochastic differential equations in finance, Math. Finance, 7 (1997), 1-71. doi: 10.1111/1467-9965.00022.
[16]	B. Han and H. Y. Wong, Backward stochastic differential equations in finance, Available at SSRN 3182387, (2019).
[17]	C. Hipp and M. Taksar, Backward stochastic differential equations in finance, Math. Finance, 26 (2000), 185-192.
[18]	J. B. Hiriart-Urruty and C. Lemaréchal, Fundamentals of Convex Analysis, Springer Science & Business Media, 2012. doi: 10.1007/978-3-642-56468-0.
[19]	B. Højgaard, Optimal dynamic premium control in non-life insurance. Maximizing dividend pay-outs, Scand. Actuar. J., 4 (2002), 225-245. doi: 10.1080/03461230110106291.
[20]	Y. Hu, H. Jin and X. Y. Zhou, Time-inconsistent stochastic linear-quadratic control, SIAM J. Control Optim., 50 (2012), 1548-1572. doi: 10.1137/110853960.
[21]	D. Li, X. Rong and H. Zhao, Time-consistent reinsurance–investment strategy for an insurer and a reinsurer with mean–variance criterion under the CEV model, J. Comput. Appl. Math., 283 (2015), 142-162. doi: 10.1016/j.cam.2015.01.038.
[22]	Y. Li and Z. Li, Optimal time-consistent investment and reinsurance strategies for mean–variance insurers with state dependent risk aversion, Insurance Math. Econom., 53 (2013), 86-97. doi: 10.1016/j.insmatheco.2013.03.008.
[23]	Z. Li, Y. Zeng and Y. Lai, Optimal time-consistent investment and reinsurance strategies for insurers under Heston's SV model, Insurance Math. Econom., 51 (2012), 191-203. doi: 10.1016/j.insmatheco.2011.09.002.
[24]	X. Liang and V. R. Young, Minimizing the probability of ruin: Optimal per-loss reinsurance, Insurance Math. Econom., 82 (2018), 181-190. doi: 10.1016/j.insmatheco.2018.07.005.
[25]	Z. Liang and E. Bayraktar, Optimal reinsurance and investment with unobservable claim size and intensity, Insurance Math. Econom., 55 (2014), 156-166. doi: 10.1016/j.insmatheco.2014.01.011.
[26]	H. Markowitz, Portfolio selection, The Journal of Finance, 7 (1952), 77-91.
[27]	F. Menoncin and E. Vigna, Mean-variance target-based optimisation for defined contribution pension schemes in a stochastic framework, Insurance Math. Econom., 76 (2017), 172-184. doi: 10.1016/j.insmatheco.2017.08.002.
[28]	D. S. Promislow and V. R. Young, Minimizing the probability of ruin when claims follow Brownian motion with drift, N. Am. Actuar. J., 9 (2005), 110-128. doi: 10.1080/10920277.2005.10596214.
[29]	Y. Shen and Y. Zeng, Optimal investment–reinsurance strategy for mean–variance insurers with square-root factor process, Insurance Math. Econom., 62 (2015), 118-137. doi: 10.1016/j.insmatheco.2015.03.009.
[30]	R. H. Strotz, Myopia and inconsistency in dynamic utility maximization, The Review of Economic Studies, 23 (1955), 128-143. doi: 10.1007/978-1-349-15492-0_10.
[31]	H. Wang, R. Wang and J. Wei, Time-consistent investment-proportional reinsurance strategy with random coefficients for mean–variance insurers, Insurance Math. Econom., 85 (2019), 104-114. doi: 10.1016/j.insmatheco.2019.01.002.
[32]	T. Wang and J. Wei, Time-consistent mean–variance asset–liability management with random coefficients, Insurance Math. Econom., 77 (2017), 84-96. doi: 10.1016/j.insmatheco.2017.08.011.
[33]	T. Yan and H. Y. Wong, Open-loop equilibrium reinsurance-investment strategy under mean–variance criterion with stochastic volatility, Insurance Math. Econom., 90 (2020), 105-119. doi: 10.1016/j.insmatheco.2019.11.003.
[34]	H. Yang and L. Zhang, Optimal investment for insurer with jump-diffusion risk process, Insurance Math. Econom., 37 (2005), 615-634. doi: 10.1016/j.insmatheco.2005.06.009.
[35]	J. Yong, Linear-quadratic optimal control problems for mean-field stochastic differential equations-time-consistent solutions, Trans. Amer. Math. Soc., 369 (2017), 5467-5523. doi: 10.1090/tran/6502.
[36]	Y. Zeng and Z. Li, Optimal time-consistent investment and reinsurance policies for mean-variance insurers, Insurance Math. Econom., 49 (2011), 145-154. doi: 10.1016/j.insmatheco.2011.01.001.
[37]	Y. Zeng, Z. Li and Y. Lai, Time-consistent investment and reinsurance strategies for mean–variance insurers with jumps, Insurance Math. Econom., 52 (2013), 498-507. doi: 10.1016/j.insmatheco.2013.02.007.
[38]	L. Zhang, R. Wang and J. Wei, Optimal mean-variance reinsurance and investment strategy with constraints in a non-Markovian regime-switching model, Stat. Theory Relat. Fields, 4 (2020), 214-227. doi: 10.1080/24754269.2020.1719356.
[39]	X. Zhang, H. Meng and Y. Zeng, Optimal investment and reinsurance strategies for insurers with generalized mean–variance premium principle and no-short selling, Insurance Math. Econom., 67 (2016), 125-132. doi: 10.1016/j.insmatheco.2016.01.001.
[40]	H. Zhao, Y. Shen and Y. Zeng, Time-consistent investment-reinsurance strategy for mean-variance insurers with a defaultable security, J. Math. Anal. Appl., 437 (2016), 1036-1057. doi: 10.1016/j.jmaa.2016.01.035.