THE BENEFITS OF FAMILY ANNUITY CALCULATION WITH VINE’S COPULA AND FUZZY INTEREST RATE

  • Kurnia Novita Sari Actuarial Science, Bandung Institute of Teknologi, Indonesia
  • Randi Deautama Actuarial Science, Bandung Institute of Teknologi, Indonesia
  • Ady Febrisutisyanto Actuarial Science, Bandung Institute of Teknologi, Indonesia
Keywords: Family Annuity, Vine Copula, Fuzzy, Benefit

Abstract

One example of a multiple life annuity product (covering more than one person) is a reversionary annuity, which is a life annuity product for two or more annuitants whose annuity payments will begin after one of the annuitants specified in the contract dies first until the other annuitant also dies. This type of annuity is modified into a family annuity consisting of husband, wife, and child. The marginal distribution is constructed from a combined model of several mortality models such as Heligman-Pollard, Costakis, and Kannisto-Makeham models to capture mortality at young and old ages.This study takes this dependency into account when modeling the joint distribution of remaining life expectancy between the parties. The joint distribution of remaining lifetime between annuitants is modeled with a Vine’s copula constructed from the marginal distribution of each annuitant. This research also takes account the actuarial margin rate using BI-7-day (reverse) repo rate data estimated with fuzzy sets. The annuity benefits calculation is assumed with some Kendall's tau () values. The result shows the value of annuity benefits increases as the value of  increases.

Downloads

Download data is not yet available.

References

H. U. Gerber, Life Insurance Mathematics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. doi: 10.1007/978-3-662-03460-6.

N. L. Bowers, H. U. Gerber, J. C. Hickman, D. A. Jones, and C. J. Nesbitt, Actuarial Mathematics, Second. The Society of Actuaries, 1997.

H. Li, K. S. Tan, S. Tuljapurkar, and W. Zhu, “Gompertz Law Revisited: Forecasting Mortality with a Multi-factor Exponential Model,” SSRN Electronic Journal, 2019, doi: 10.2139/ssrn.3495369.

M. Böhnstedt, J. Gampe, and H. Putter, “Information measures and design issues in the study of mortality deceleration: findings for the gamma-Gompertz model,” Lifetime Data Anal, vol. 27, no. 3, pp. 333–356, Jul. 2021, doi: 10.1007/s10985-021-09518-4.

F. Dufresne, E. Hashorva, G. Ratovomirija, and Y. Toukourou, “On age difference in joint lifetime modelling with life insurance annuity applications,” Annals of Actuarial Science, vol. 12, no. 2, pp. 350–371, Sep. 2018, doi: 10.1017/S1748499518000076.

K. Henshaw, C. Constantinescu, and O. M. Pamen, “Stochastic mortality modelling for dependent coupled lives,” Risks, vol. 8, no. 1, Mar. 2020, doi: 10.3390/risks8010017.

C. Czado, Analyzing Dependent Data with Vine Copulas, vol. 222. Cham: Springer International Publishing, 2019. doi: 10.1007/978-3-030-13785-4.

J. De Andreas-Sanchez and L. G. Puchades, “Some computational results for the fuzzy random value of life actuarial liabilities,” Iranian Journal of Fuzzy Systems, vol. 4, pp. 1–25, 2017.

M. Aalaei, “Using Fuzzy Interest Rates for Uncertainty Modelling in Enhanced Annuities Pricing,” International Journal of Mathematical Modelling & Computations, vol. 12, no. 04, pp. 265–274, 2022, doi: 10.30495/ijm2c.2023.1968679.1262.

L. Heligman and J. H. Pollard, “The age pattern of mortality,” J Inst Actuar, vol. 107, no. 1, pp. 49–80, Jan. 1980, doi: 10.1017/S0020268100040257.

A. Kostaki, “A nine‐parameter version of the Heligman‐Pollard formula,” Math Popul Stud, vol. 3, no. 4, pp. 277–288, Jul. 1992, doi: 10.1080/08898489209525346.

A. R. Thatcher, V. Kannisto, and J. W. Vaupel, “The force of mortality at ages 80 to 120,” Odense, Denmark, 1998.

T. I. Missov, A. Lenart, L. Nemeth, V. Canudas-Romo, and J. W. Vaupel, “The gompertz force of mortality in terms of the modal age at death,” Demogr Res, vol. 32, no. 1, pp. 1031–1048, 2015, doi: 10.4054/DemRes.2015.32.36.

R. B. Nelsen, An Introduction to Copulas, Second. New York: Springer Series in Statistics, NY: Springer New York, 2006. doi: 10.1007/0-387-28678-0.

A. G. Aggarwal and A. Sharma, “An Innovative B2C E-commerce Websites Selection using the ME-OWA and Fuzzy AHP,” Jan. 2018, pp. 13–19. doi: 10.15439/2017KM37.

N. M. M. Noor, A. Retnowardhani, M. L. Abd, and M. Y. M. Saman, “Crime Forecasting using ARIMA Model and Fuzzy Alpha-cut.,” Journal of Applied Sciences, vol. 13, no. 1, 2013.

I. Mircea and M. Covrig, “A Discrete Time Insurance Model with Reinvested Surplus and a Fuzzy Number Interest Rate,” Procedia Economics and Finance, vol. 32, pp. 1005–1011, 2015, doi: 10.1016/s2212-5671(15)01561-0.

F. Huang, R. Maller, and X. Ning, “Modelling life tables with advanced ages: An extreme value theory approach,” Insur Math Econ, vol. 93, pp. 95–115, Jul. 2020, doi: 10.1016/j.insmatheco.2020.04.004.

H. Akaike, “Information Theory and an Extension of the Maximum Likelihood Principle,” Proceedings of the Second International Symposium on Information Theory Budapest, 1973.

Published
2023-12-19
How to Cite
[1]
K. Sari, R. Deautama, and A. Febrisutisyanto, “THE BENEFITS OF FAMILY ANNUITY CALCULATION WITH VINE’S COPULA AND FUZZY INTEREST RATE”, BAREKENG: J. Math. & App., vol. 17, no. 4, pp. 2461-2470, Dec. 2023.