Stochastic Interest Rate Models in Pension Fund Valuation: A Comparative Study of Vasicek and Cox-Ingersoll-Ross under the Aggregate Cost Method

Authors

  • Angel Tiovanny Universitas Sumatera Utara
  • Maulida Yanti Universitas Sumatera Utara
  • Citra Dewi Hasibuan Universitas Sumatera Utara

DOI:

https://doi.org/10.32734/jormtt.v8i1.22753

Keywords:

Vasicek Model, Cox-Ingersoll-Ross Model, Pension Fund, Aggregate Cost Method, Interest Rate

Abstract

This research aims to implement the Vasicek and Cox-Ingersoll-Ross (CIR) interest rate models in pension fund calculations using the Aggregate Cost method. These interest rate models are utilized to manage the uncertainty of interest rates influenced by inflation, providing a more realistic projection of future pension contributions. The analyzed data includes the Indonesian Mortality

Table IV and historical BI-Rate interest rate data from January 2015 to December 2024. Parameter estimation for the Vasicek and CIR models is conducted using the Maximum Likelihood Estimation (MLE) method, and long-term interest rate simulations are performed using the Milstein method. The findings indicate that the Vasicek model has a MAPE of 14.97%, while the CIR model has a MAPE of 19.06%. Pension fund calculations are conducted for participants with starting ages of 25, 30, and 35 years, revealing that the use of Vasicek and CIR interest rate models yields higher present value of pension benefit compared to a constant

interest rate. Additionally, normal contributions for younger participants tend to be lower due to longer working periods. The study concludes that the Vasicek model is more accurate in estimating interest rates for pension fund calculations.

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Published

2026-05-20

How to Cite

[1]
A. Tiovanny, M. Yanti, and C. D. Hasibuan, “Stochastic Interest Rate Models in Pension Fund Valuation: A Comparative Study of Vasicek and Cox-Ingersoll-Ross under the Aggregate Cost Method”, J. of Research in Math. Trends and Tech., vol. 8, no. 1, May 2026.

Issue

Vol. 8 No. 1 (2026): Journal of Research in Mathematics Trends and Technology (JoRMTT)

Section

Articles

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