[1] Dacorogna, M. M., & Kratz, M. (2015). Living in a stochastic world and managing complex risks. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2668468
[2] Bis, D. (2013). Longevity risk transfer markets: market structure, growth drivers and impediments, and potential risks [presentation]. Joint forum, basel committee on banking supervision, bank for international settlements (pp. 1–35).
[3] de Moivre, A. (1718). The doctrine of chances: or, a method of calculating the probabilities of events in play (Vol. 200). Chelsea Publishing Company.
[4] Gompertz, B. (1825). XXIV. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philosophical transactions of the royal society of london, (115), 513–583.
[5] Makeham, W. M. (1860). On the law of mortality and the construction of annuity tables. Journal of the institute of actuaries, 8(6), 301–310.
[6] Tabeau, E. (2001). A review of demographic forecasting models for mortality. In Forecasting mortality in developed countries: insights from a statistical, demographic and epidemiological perspective (pp. 1-32). Springer.
[7] Lee, R. D., & Carter, L. R. (1992). Modeling and forecasting U.S. mortality. Journal of the american statistical association, 87(419), 659–671. DOI:10.1080/01621459.1992.10475265
[8] Christiansen, M. C., Denuit, M. M., & Lazar, D. (2012). The solvency II square-root formula for systematic biometric risk. Insurance: mathematics and economics, 50(2), 257–265. DOI:https://doi.org/10.1016/j.insmatheco.2011.11.008
[9] Moosavi, S. S., & Payandeh Najafabadi, A. (2020). Modelling the laboratory health costs during 2015 to 2019. Iranian journal of health insurance, 3(3), 200–209.
[10] Renshaw, A. E., & Haberman, S. (2006). A cohort-based extension to the Lee–Carter model for mortality reduction factors. Insurance: mathematics and economics, 38(3), 556–570. DOI:https://doi.org/10.1016/j.insmatheco.2005.12.001
[11] Currie, I. D. (2016). On fitting generalized linear and non-linear models of mortality. Scandinavian actuarial journal, 2016(4), 356–383. DOI:10.1080/03461238.2014.928230
[12] Currie, I. D., Durban, M., & Eilers, P. H. C. (2004). Smoothing and forecasting mortality rates. Statistical modelling, 4(4), 279–298.
[13] Perkes, A. C. (1982). The development and field testing of an instrument to measure apprehension toward animals. School science and mathematics, 82(2), 157–162.
[14] Wong-Fupuy, C., & Haberman, S. (2004). Projecting mortality trends: recent developments in the United Kingdom and the United States. North american actuarial journal, 8(2), 56–83. DOI:10.1080/10920277.2004.10596137
[15] Plat, R. (2009). On stochastic mortality modeling. Insurance: mathematics and economics, 45(3), 393–404. DOI:10.1016/j.insmatheco.2009.08.006
[16] Booth, H., & Tickle, L. (2008). Mortality modelling and forecasting: a review of methods. Annals of actuarial science, 3(1–2), 3–43. DOI:DOI: 10.1017/S1748499500000440
[17] Dowd, K., Cairns, A. J. G., Blake, D., Coughlan, G. D., Epstein, D., & Khalaf-Allah, M. (2010). Backtesting stochastic mortality models: an ex post evaluation of multiperiod-ahead density forecasts. North American actuarial journal, 14(3), 281–298.
[18] Cox, J. C., Ingersoll, J. E., & Ross, S. A. (2005). A theory of the term structure of interest rates. In Theory of valuation (pp. 129–164). World Scientific. DOI: doi:10.1142/9789812701022_0005
[19] Pitacco, E. (2009). Modelling longevity dynamics for pensions and annuity business. Oxford University Press.
[20] Lorenzo, E. Di, Sibillo, M., & Tessitore, G. (2006). A stochastic proportional hazard model for the force of mortality. Journal of forecasting, 25(7), 529–536.
[21] Cox, J. C., Ingersoll Jr, J. E., & Ross, S. A. (1985). An intertemporal general equilibrium model of asset prices. Econometrica: journal of the econometric society, 363–384.
[22] Kladıvko, K. (2007). Maximum likelihood estimation of the Cox-Ingersoll-Ross process: the Matlab implementation. Technical computing prague, 1(3), 1–9.
[23] Glasserman, P. (2004). Monte Carlo methods in financial engineering (Vol. 53). Springer.
[24] Shoaee, S., & Gholi Keshmarzi, M. M. (2021). Analysis of the stochastic mortality models based on lee-carter model in predicting mortality rates in life and health insurance. Iranian journal of health insurance, 4(1), 68–79.
[25] Database, H. M. (2020). University of California, Berkeley (USA), and Max planck institute for demographic research (Germany). https://www.mortality.org/
[26] Zokaei, M., & Maghsoudi, M. (2011). Reconstruction of frailty-based mortality models by a generalisation of gompertz distribution. Sanaat-e-bimeh, 25(4), 59–85.
[27] Hassan Zadeh, A., & Daraee, D. (2020). Mortality rates estimation of active insured members of social insurance fund of farmers, villagers and Tribes in 2016. Iranian journal of health insurance, 3(1), 57–66.
[28] Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79–82.
[29] Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the american statistical association, 90(430), 773–795. DOI:10.1080/01621459.1995.10476572
[30] Box, G. E. P. (1976). Science and statistics. Journal of the american satistical association, 71(356), 791–799.