Actuarial (Mathematical) Modeling of Mortality and Survival Curves

Patrick L. Brockett () and Yuxin Zhang ()
Additional contact information
Patrick L. Brockett: University of Texas at Austin, Department of Information, Risk and Operations Management
Yuxin Zhang: Wayne State University, Department of Management and Information Systems

Chapter 61 in Handbook of the Mathematics of the Arts and Sciences, 2021, pp 1559-1591 from Springer

Abstract: Abstract This chapter reviews the history, development, and application of mathematical mortality models. It consists of four sections. The first section focuses on parametric mortality models for single individuals. It starts with the innovation involved in creating a mathematical model of mortality, the importance, and the application of mortality tables, followed by the mathematical evolution of mortality models. It ends with the most recent model formulation – the stochastic mortality model. Next, the second section introduces parametric joint mortality models for coupled lives. It covers both the deterministic and stochastic model settings. Joint mortality models for coupled lives have great potential in the insurance industry for pricing joint and last survivor life insurance and annuity products (including pension products). As shown in previous studies, health condition and the expected life length of an individual can be significantly impacted by his or her significant partner, so consideration of bivariate mortality models can have significant practical importance. The third section summarizes nonparametric mortality models, for both individual and coupled lives. Compared with parametric models, the nonparametric ones are free of distributional assumptions and accordingly have a wider and more robust applications when understanding and parametrically modeling the true mortality distribution is not the focus of the study (e.g., when the focus is on studying the effects of covariates on mortality progression rather than mortality itself). Finally, in the fourth section, longevity risk and longevity risk models are introduced. These models are different from the mathematical models in previous sections in that these longevity risk models describe the changes of life expectancy between generations of individuals taking into consideration the cohort effects of people born in different years. These models have a great potential in solving longevity risk problems such as those occur in individual financial planning, institutional portfolio management, social security and medical insurance at a country level.

Keywords: Mortality modeling; Mathematical models of survival; Modeling dependence in joint life survival; Biological inspired mathematical mortality models (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-57072-3_69

Ordering information: This item can be ordered from
http://www.springer.com/9783319570723

DOI: 10.1007/978-3-319-57072-3_69

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().