A comparison of Vasicek and Cox-Ingersol-Ross models in determination of reserves for a term assurance policy

Mercy Jepchumba, Joshua Were

Article ID: 8635
Vol 8, Issue 1, 2025


Abstract


Interest rates influence the calculation of premiums, reserves and benefits in the long-term. Theoretically, calculation of such actuarial values is based on the assumption of constant interest rates although interest rates constantly move over time. To obtain a more realistic assessment in valuation, it is beneficial if stochastic interest rates are used. Accurate calculation of reserves ensures that the insurance company can pay claims. A Reserve is a sum of money held by a financial institution such as a life office or a pension fund to cover for the difference between present value of future liabilities including expenses and present value of future premiums. A term insurance contract is an insurance policy that pays the sum assured to the beneficiaries if the policyholder dies within the duration of the policy. The purpose of this study was to compare the reserves that would be needed for a term insurance policy using the Vasicek and the Cox-Ingersol-Ross models. The models were chosen and are widely used because they are tractable and their ease of implementation. The stages of this research activity started by estimating the parameters for the models using Maximum Likelihood Estimation Method. Interest rates were then simulated for the models under study. Next, the reserve value of term insurance policy was determined using the simulated interest rates for the models using the Prospective Method for four randomly generated people of different ages. At the final stage, the results of the reserve values for the models were interpreted and compared. Kenya’s Life Table 2001–2003 was used as the reference in determination of mortality assumptions in reserve calculation. The goodness of fit of the models were done using Likelihood Ratio Test and CIR model was a better fit for the data. Interest rates were highly volatile, a feature replicated better by CIR model. Reserve values were also high for CIR model. Reserve values were higher for male insureds due to a higher mortality rate for men than women while the benefit reserves for the younger age were lower as compared to the older ages.

Keywords


reserve; stochastic; term insurance

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References


1. Dickson DCM, Hardy MR, Waters HR. Actuarial Mathematics for Life Contingent Risks. New York (NY): Cambridge University Press; 2009.

2. Eckert C. Dealing with Low Interest Rates in Life Insurance: An Analysis of Additional Reserves in the German Life Insurance Industry. Journal of Risk and Financial Management. 2019; 12(3): 119. doi: 10.3390/jrfm12030119

3. Mendis A. Study of Volatility Stochastic Processes in the Context of Solvency Forecasting for Sri Lankan Life Insurers. Open Journal of Statistics. 2021; 11(01): 77-98. doi: 10.4236/ojs.2021.111004

4. Zillmer A. Contributions to the theory of life insurance premium reserves. Press of Theodore von der Nahmer; 1863

5. Noviyanti L, Syamsuddin M. Life Insurance with Stochastic Interest Rates. Persatuan Aktuaris Indonesia. 2016.

6. Jere S, Offen ER, Basmanebothe O. Optimal Investment, Consumption and Life Insurance Problem with Stochastic Environments. Journal of Mathematics Research. 2022; 14(4): 33. doi: 10.5539/jmr.v14n4p33

7. Muthee SK. A stochastic approach in determining claims reserve in general insurance [PHD thesis]. University of Nairobi; 2009.

8. Kamila I, Andriyati A, Rohaeti E. A comparison benefit reserves of an n–year term life insurance between using the vasicek model and cox-ingersoll-ross model. Desimal: Jurnal Matematika. 2024; 7(1): 17-24.

9. Norberg R. Reserves in Life and Pension Insurance. Scandinavian Actuarial Journal. 1991; 1991(1): 3-24. doi: 10.1080/03461238.1991.10557357

10. Martellini L, Priaulet P, Fabozzi FJ, et al. Hedging interest rate risk with term structure factor models. Advanced Bond Portfolio Management: Best Practices in Modeling and Strategies. 2012; 267-89.

11. Dufresne D. Stochastic life annuities. North American Actuarial Journal. 2007; 11(1): 136-57.

12. Hussain J, Soomro MA, Dahri SA, et al. A study of maximizing skew Brownian motion with applications to option pricing. Journal of Radiation Research and Applied Sciences. 2024; 17(1): 100732.

13. Ochieng OS. Factors Contributing to Financial Distress in Commercial Banks of Kenya. The International Journal of Business Management and Technology. 2018; 2(5): 135-50.

14. Brigo D, Mercurio F. Interest rate models—theory and practice. Springer; 2007.

15. Maybeck PS. Stochastic Models, Estimation, and Control. New York: Academic Press; 1979.

16. Bernal V. Calibration of the Vasicek model: a step by step guide. Available online: https://www.scribd.com/document/446870757/Calibration-of-the-Vasicek-Model-pdf (accessed on 6 August 2024).

17. Ben Salah M, Abid F. An Empirical Comparison of the Short Term Interest Rate Models. SSRN Electronic Journal. 2012. doi: 10.2139/ssrn.2400433

18. Cox JC, Ingersoll JE, Ross SA. A Theory of the Term Structure of Interest Rates. Econometrica. 1985; 53(2): 385. doi: 10.2307/1911242

19. Orlando G, Mininni RM, Bufalo M. Interest rates calibration with a CIR model. The Journal of Risk Finance. 2019; 20(4): 370-387. doi: 10.1108/jrf-05-2019-0080

20. Ana P, Mariana G. Premiums Calculation For Life Insurance. Annals of the University of Petrosani, Economics. 2012; 12(3): 197-204.

21. Central Bank of Kenya. Treasury Bills. Available online: https://www.centralbank.go.ke/bills-bonds/treasury-bills/ (accessed on 6 August 2024).

22. Wurren DB. A discussion of negative reserves. The Actuary. 1986; 2(8): 4–5.

23. Orlando G, Mininni RM, Bufalo M. Forecasting interest rates through Vasicek and CIR models: A partitioning approach. Journal of Forecasting. 2020; 39(4): 569-579. doi: 10.1002/for.2642.




DOI: https://doi.org/10.24294/fsj8635

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