Document Type : Research Article
Author
Insurance research center, Tehran, Iran
Abstract
In this paper, fuzzy set theory is implemented to model internal rate of return for calculating the price of life settlements. Deterministic, probabilistic and stochastic approaches is used to price life settlements in the secondary market for the Iranian insurance industry. Research findings were presented and analyzed for whole life insurance policies using the interest rates announced in the supplement of Regulation No. 68 and Iranian life table, which recently has been issued to be used by insurance companies. Also, the results of three approaches were compared with surrender value, which indicates the surrender value is lower than the fuzzy price calculated based on the probabilistic and stochastic approaches and it is higher than the price calculated based on the deterministic approach. Therefore, selling life settlements in the secondary market in Iran based on calculated fuzzy price using probabilistic and stochastic approaches will benefit the policyholder. Also, the price is obtained in the form of an interval using the fuzzy sets theory and the investor can decide which price is suitable for this policy based on financial knowledge. Furthermore, in order to show validity of the proposed fuzzy method, the findings are compared to the results of using the random internal rate of return.
Keywords
[2] M. Aalaei, Investigating the Impact of Using Iranian Life Table on Standard and Modi ed Premium of Various Life Insurance Products, Journal of Population Association of Iran (2022), In Press. (In Persian)
[3] American Cancer Society, Survival Rates for Laryngeal and Hypopharyngeal Cancers, (2019).
[4] J. Bhattacharya, D. Goldman and N. Sood, Price regulation in secondary insurance markets, Journal of Risk and Insurance, 71(4) (2004), pp. 643-675.
[5] L. Bian and Z. Li, Fuzzy simulation of European option pricing using sub-fractional Brownian motion, Chaos, Solitons Fractals, 153(2) (2021), 111442.
[6] V. Dedes, How to determine fair value for life insurance policies in a secondary market, (2011).
[7] D. C. M. Dickson, M. R. Hardy and H. R. Waters, Actuarial mathematics for life contingent risks, Cambridge university press, Third edition, (2020).
[8] V.F. Dolan, Advantages of a Life Expectancy Using Life Insurance Underwriting and Life Settlement Methods in the Legal Setting, Las Vegas: VFD Consulting, Inc. (2013).
[9] S. Ghasemalipour and B. Fathi-Vajargah, Fuzzy simulation of European option pricing using mixed fractional Brownian motion, Soft Computing, 23(24) (2019), pp. 13205-13213.
[10] J.A. Sanches, Fuzzy Claim Reserving In Non-Life Insurance, Computer Science and Information Systems, 11(2) (2014), pp. 825838.
[11] J.A. Sanches and L.G. Puchades, Using fuzzy random variables in life annuities pricing, Fuzzy Sets and Systems, 188 (2012), pp. 2744.
[12] J.A. Sanches and L.G. Puchades, Some computational results for the fuzzy random value of life actuarial liabilities, Iranian Journal of Fuzzy Systems, 14(4) (2017), pp. 1-25.
[13] J.A. Sanches and L.G. Puchades, Life settlements: descriptive analysis and quantitative aspects, Management Letters, 21(2) (2021), pp. 19-34. (In Spain)
[14] J.A. Sanches, L.G. Puchades and A. Zhang, Incorporating Fuzzy Information in Pricing Substandard Annuities, Computers Industrial Engineering, 145 (2020), 106475.
[15] J. Xu, Dating death: An empirical comparison of medical underwriters in the US life settlements market, North American Actuarial Journal, 24(1) (2020), pp. 36-56.
[16] C. You and L. Bo, Pricing of European call option under fuzzy interest rate, Journal of Industrial and Management Optimization (2022), In Press.
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