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The lifecontingencies Package: Performing Financial and Actuarial Mathematics Calculations in R

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  • Spedicato, Giorgio Alfredo

Abstract

It is possible to model life contingency insurances with the lifecontingencies R package, which is capable of performing financial and actuarial mathematics calculations. Its functions permit one to determine both the expected value and the stochastic distribution of insured benefits. Therefore, life insurance coverage can be priced and portfolios risk-based capital requirements can be assessed. This paper briefly summarizes the theory regarding life contingencies that is based on financial mathematics and demographic con- cepts. Then, with the aid of applied examples, it shows how the lifecontingencies package can be a useful tool for executing routine, deterministic, or stochastic calculations for life-contingencies actuarial mathematics.

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  • Spedicato, Giorgio Alfredo, 2013. "The lifecontingencies Package: Performing Financial and Actuarial Mathematics Calculations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i10).
    • Handle: RePEc:jss:jstsof:v:055:i10
    DOI: http://hdl.handle.net/10.18637/jss.v055.i10
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    1. Thomas Post & Helmut Gründl & Lisa Schmidl & Mark S. Dorfman, 2007. "Implications of IFRS for the European Insurance Industry—Insights From Capital Market Theory," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 10(2), pages 247-265, September.
    2. Dutang, Christophe & Goulet, Vincent & Pigeon, Mathieu, 2008. "actuar: An R Package for Actuarial Science," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i07).
    3. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
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    2. Olivier Cabrignac & Arthur Charpentier & Ewen Gallic, 2020. "Modeling Joint Lives within Families," Working Papers halshs-02871927, HAL.
    3. Lu Xiong & Vajira Manathunga & Jiyao Luo & Nicholas Dennison & Ruicheng Zhang & Zhenhai Xiang, 2023. "AutoReserve: A Web-Based Tool for Personal Auto Insurance Loss Reserving with Classical and Machine Learning Methods," Risks, MDPI, vol. 11(7), pages 1-17, July.
    4. Marcel Bräutigam & Montserrat Guillén & Jens P. Nielsen, 2017. "Facing Up to Longevity with Old Actuarial Methods: A Comparison of Pooled Funds and Income Tontines," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 42(3), pages 406-422, July.
    5. Severinsen, A. & Myrland, Ø., 2022. "ShinyRBase: Near real-time energy saving models using reactive programming," Applied Energy, Elsevier, vol. 325(C).
    6. Mariarosaria Coppola & Maria Russolillo & Rosaria Simone, 2019. "An Indexation Mechanism for Retirement Age: Analysis of the Gender Gap," Risks, MDPI, vol. 7(1), pages 1-13, February.
    7. Rachel WINGENBACH & Jong-Min KIM & Hojin JUNG, 2020. "Living Longer in High Longevity Risk," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 86(1), pages 47-86, March.

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