摘要: 本文在风险中性测度理论的框架下对经典的长寿风险衍生品——EIB/BNP型长寿债券进行了定价。文章首先使用Cairns-Blake-Dowd两因子模型,模拟预测了我国高龄人口未来的死亡率路径,然后通过引入三种相对熵,即Kullback-Leibler相对熵、Tsallis相对熵以及Rényi相对熵,并依据最小相对熵准则确定最优风险中性测度,进而完成对长寿债券的定价。在定价的过程中,本文融入已在我国市场发行的不同期限金融债的风险溢价信息,使定价结果更合理地反映我国金融市场规律。从计算方法上看,不同相对熵模型的定价保证了计算结果的稳健性和可信性;本文还发现对冲女性长寿风险所需的溢价整体上要高于对冲男性长寿风险所需的溢价,表明女性长寿风险面临的不确定性程度更大。本文还分别比较了仅融合单一市场风险价格以及考虑多个市场风险价格作为先验信息时定价结果的差异,发现定价的结果对市场信息依赖程度较高。这也表明了相对熵方法的可塑性较强,随着市场完全度的提高和我国金融市场的发展,该方法的定价结果将更趋于合理。文章最后一部分是总结和启示。

nyi相对熵

Abstract: Along with the raise of living standard and improvement of medical technology, average life expectancy in China is rising substantially. The unexpected improvement of average life expectancy renders the insurers to face the challenge of excessive annuity payout, and even brings about to bankrupt risk to them. This type of risk is called longevity risk. The studies on how to manage longevity risk is very popular all over the world, since China is not the only country suffering from longevity and aging. Many scholars point out that we can employ the special financial instruments, which are called longevity derivatives, into hedging the longevity risk. Compared with the mechanism design, previous and present studies are more interested on how to price the longevity derivatives appropriately. In this article, the typical derivative, the longevity bond is priced which is issued by European Investment Bank and BNP Paribas in year 2004. The future mortality of male and female aged people is simulated with Cairns-Blake-Dowd two-factor model. Then EIB/BNP bond is priced by using the risk-neutral measure theory. Since there are infinitely risk-neutral measures in an incomplete market, the relative entropy methods are introduced, to select the optimal risk-neutral measure. Three different types of relative entropy, namely Kullback-Leibler relative entropy, Tsallis relative entropy and Rényi,are applied in order to ensure the robustness. The pricing result also incorporating the market prices of risk as the useful information, in order to make the longevity bond rationally priced. The difference between pricing male mortality linked longevity bonds and that of female morality linked longevity bonds are compared, finding that high risk premia is required when hedging the longevity risk arising from female aged people, which means that improvement of female mortality reveals more uncertainty than that of male mortality, especially when the maturity is long. The case when incorporating unique market price of risk and the case when incorporating total 6 market prices of risk are also compared. It can be concluded that the pricing result varies greatly according to the number of market prices provided by the market. The simulation outcomes when using Kullback-Leibler relative entropy, Tsallis relative entropy and Rényi relative entropy are highly consistent. Thus it is also concluded that the when pricing longevity bonds with relative entropy approach,the outcomes rely much on the completeness of market. This indicates that relative entropy method is very flexible, and the more complete the financial market is, the more reasonable the pricing result will reveal. If the longevity risk is to be managed effectively by means of financial derivatives, the most important issue is to perfect the financial market. And since the relative entropy approach shows great superiority in pricing longevity derivatives, this method can be further developed, and wide application can be made in the field of longevity risk management.

Key words: longevity bond, risk-neutral measure, Kullback-Leibler relative entropy, Tsallis entropy, Rényi relative entropy [1] Tsai J T, Wang J L, Tzeng L Y. On the optimal product mix in life insurance companies using conditional value at risk[J]. Insurance:Mathematics and Economics, 2010, 46(1):235-241.[2] Wang Chouwen, Huang Hongchih, Hong Dechuan. A feasible natural hedging strategy for insurance companies[J]. Insurance:Mathematics and Economics, 2013, 52(3):532-541.[3] Wong A, Sherris M, Stevens R. Natural hedging strategies for life insurers:Impact of product design and risk measure[J]. Journal of Risk and Insurance, 2017, 84(1):153-175.[4] 艾蔚. 基于金融衍生工具视角的长寿风险管理[J]. 保险研究, 2011(3):36-44.[5] 尚勤. 基于投资者视角的长寿债券设计——来自EIB/BNP的案例分析[J]. 管理案例研究与评论, 2014, 7(5):384-391.[6] Blake D, Cairns A J G, Dowd K. Living with mortality:Longevity bonds and other mortality-linked securities[J]. British Actuarial Journal, 2006, 12(1):153-197.[7] Blake D, Cairns A, Dowd K, et al. Longevity bonds:financial engineering, valuation, and hedging[J]. Journal of Risk and Insurance, 2006, 73(4):647-672.[8] Friedberg L, Webb A. Life is cheap:Using mortality bonds to hedge aggregate mortality risk[J]. The BE Journal of Economic Analysis & Policy, 2007, 7(1):1785-1785.[9] Bayraktar E, Young V R. Hedging life insurance with pure endowments[J]. Insurance:Mathematics and Economics, 2007, 40(3):435-444.[10] Young V R. Pricing life insurance under stochastic mortality via the instantaneous Sharpe ratio[J]. Insurance:Mathematics and Economics, 2008, 42(2):691-703.[11] Bauer D, Börger M, Ruß J. On the pricing of longevity-linked securities[J]. Insurance:Mathematics and Economics, 2010, 46(1):139-149.[12] Wang S S. A class of distribution operators for pricing financial and insurance risks[J]. The Journal of Risk and Insurance, 2000,67(1):15-36.[13] Wang S S. A universal framework for pricing financial and insurance risks[J]. ASTIN Bulletin, 2002,32(2):213-234.[14] Pelsser A. On the applicability of the wang transform for pricing financial risks[J]. ASTIN Bulletin,2008,38:171-181.[15] Bauer D, Börger M,Rub J. On the pricing of longevity-linked securities[J]. Insurance:Mathematics and Economics,2010,46(1):139-149.[16] Chen Bingzheng, Zhang Lihong, Zhao Lin. On the robustness of longevity risk pricing[J]. Insurance:Mathematics and Economics, 2010, 47(3):358-373.[17] Boyer M M, Stentoft L. If we can simulate it, we can insure it:An application to longevity risk management[J]. Insurance:Mathematics and Economics, 2013, 52(1):35-45.[18] Dahl M, Møller T. Valuation and hedging of life insurance liabilities with systematic mortality risk[J]. Insurance:mathematics and economics, 2006, 39(2):193-217.[19] Dahl M, Melchior M, Møller T. On systematic mortality risk and risk-minimization with survivor swaps[J]. Scandinavian Actuarial Journal, 2008, 2008,(2-3):114-146.[20] Blackburn C, Sherris M. Consistent dynamic affine mortality models for longevity risk applications[J]. Insurance:Mathematics and Economics, 2013, 53(1):64-73.[21] Yang Bowen, Li J, Balasooriya U. Using bootstrapping to incorporate model error for risk-neutral pricing of longevity risk[J]. Insurance:Mathematics and Economics, 2015, 62:16-27.[22] Li J S H. Pricing longevity risk with the parametric bootstrap:A maximum entropy approach[J].Insurance:Mathematics and Economics, 2010, 47(2):176-186.[23] 樊毅. 基于随机动态死亡率模型的长寿风险债券定价研究[D].长沙:湖南大学,2017.[24] Gompertz.On the nature of the function expressive of the law of human mortality and on a new model of determining life contingencies[J]. Philosophical Transactions of Royal Scociety of London,1825,115:513-585.[25] Heligman H, Pollard J. The age pattern of mortality[J].Journal of the institute of Actuaries,1980,107:49-80.[26] Carriere J F. Parametric models for life tables[J]. Transaction society of actuaries, Vol. 44:77-79,1992.[27] Lee R D, Carter L R. Modeling and forecasting US mortality[J]. Journal of the American statistical association, 1992, 87(419):659-671.[28] Cairns A J G, Blake D, Dowd K. A two-factor model for stochastic mortality with parameter uncertainty:Theory and calibration[J]. Journal of Risk and Insurance, 2006, 73(4):687-718.[29] Yang S S, Wang C W. Pricing and securitization of multi-country longevity risk with mortality dependence[J]. Insurance Mathematics & Economics, 2013, 52(2):157-169.[30] Chen Hua, MacMinn R D, Sun Tao. Multi-population mortality models:A factor copula approach[J]. Insurance:Mathematics and Economics, 2015, 63:135-146.[31] Chen Hua, MacMinn R D, Sun Tao. Mortality dependence and longevity bond pricing:A dynamic factor copula mortality model with the GAS structure[J]. Journal of Risk and Insurance, 2017, 84(S1):393-415.[32] 尚勤,秦学志,周颖颖.死亡强度服从Ornstein-Uhlenbeck跳过程的长寿债券定价模型[J].系统管理学报,2008,(3):297-302.[33] 尚勤,张国忠,胡友群,等.基于Cameron-Martin-Girsanov理论的长寿债券定价模型[J].系统管理学报,2013,22(4):472-476+486.[34] 郑玮,柴柯辰,钱林义.同出生年死亡率相关性效应下的长寿债券定价研究[J].应用概率统计,2014,30(1):72-83.[35] 巢文,邹辉文.基于双指数跳跃扩散模型的长寿债券定价研究[J].中国管理科学,2017,25(9):46-52.[36] 樊毅,张宁,王耀中.基于双因素Wang转换方法的长寿风险债券定价研究[J].财经理论与实践,2017,38(4):32-38.[37] 田玲,姜世杰,樊毅.基于风险立方方法的长寿风险债券定价研究[J].保险研究,2017,(7):3-12.[38] Brockett P L. Information theoretic approach to actuarial science:A unification and extension of relevant theory and applications[J]. Transactions of the Society of Actuaries, 1991, 43:73-135.[39] Kullback S, Leibler R A. On information and sufficiency[J]. The annals of mathematical statistics, 1951, 22(1):79-86.[40] Tsallis C. Generalized entropy-based criterion for consistent testing[J]. Physical Review E, 1998, 58(2):1442.[41] Rényi A. On measures of entropy and information[C]//Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1:Contributions to the Theory of Statistics. The Regents of the University of California, 1961.[42] 柳向东,王星蕊.半马氏市道轮换利率期限结构模型——基于最小Tsallis熵鞅测度[J].系统工程理论与实践,2017,37(5):1136-1143.[43] Dolan C, Blanchet J, Iyengar G, et al. A model robust real options valuation methodology incorporating climate risk[J]. Resources Policy, 2018,57:81-87. 地址:北京市海淀区中关村东路55号 北京8712信箱《中国管理科学》编辑部 邮编:100190
电话:010-62542629 E-mail:[email protected] 本系统由北京玛格泰克科技发展有限公司设计开发