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A Regression Based Approach for Valuing Longevity Measures

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Anna Rita Bacinello

    (University of Trieste, Department of Economics, Business, Mathematics and Statistics ‘B. de Finetti’)

  • Pietro Millossovich

    (University of Trieste, Department of Economics, Business, Mathematics and Statistics ‘B. de Finetti’
    University of London, Faculty of Actuarial Science and Insurance, Bayes Business School, City)

  • Fabio Viviano

    (University of Trieste, Department of Economics, Business, Mathematics and Statistics ‘B. de Finetti’
    University of Udine, Department of Economics and Statistics)

Abstract

This paper addresses the ever-prominent issue of how to evaluate and forecast future longevity dynamics. Indeed, studying the evolution of mortality and/or the cost of longevity risk is a major task for both demographers and actuaries. In contrast to the usual period-based evaluation, we consider the problem of approximating the distribution of future life expectancy with a cohort-based perspective. In particular, we suggest an application of the Least-Squares Monte Carlo approach, which allows to overcome the straightforward nested simulations method. The method is applied to the family of CBDX models, and results and comparisons between different models, males and females, and period and cohort approaches, are presented.

Suggested Citation

  • Anna Rita Bacinello & Pietro Millossovich & Fabio Viviano, 2022. "A Regression Based Approach for Valuing Longevity Measures," Springer Books, in: Marco Corazza & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 44-49, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-99638-3_8
    DOI: 10.1007/978-3-030-99638-3_8
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