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Social Security Benefit Valuation, Risk, and Optimal Retirement

Author

Listed:
  • Yassmin Ali

    (New Jersey Institute of Technology, Ying Wu College of Computing, 186 Bleeker St., Newark, NJ 07102, USA)

  • Ming Fang

    (New Jersey Institute of Technology, Martin Tuchman School of Management, 3000 Central Avenue Building (CAB), Newark, NJ 07102, USA)

  • Pablo A. Arrutia Sota

    (New Jersey Institute of Technology, Martin Tuchman School of Management, 3000 Central Avenue Building (CAB), Newark, NJ 07102, USA)

  • Stephen Taylor

    (New Jersey Institute of Technology, Martin Tuchman School of Management, 3000 Central Avenue Building (CAB), Newark, NJ 07102, USA
    Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University, Sokolovska 83, 186 75 Prague, Czech Republic)

  • Xun Wang

    (New Jersey Institute of Technology, Martin Tuchman School of Management, 3000 Central Avenue Building (CAB), Newark, NJ 07102, USA)

Abstract

We develop valuation and risk techniques for the future benefits of a retiree who participates in the American Social Security program based on their chosen date of retirement, the term structure of interest rates, and forecasted life expectancy. These valuation methods are then used to determine the optimal retirement time of a beneficiary given a specific wage history and health profile in the sense of maximizing the present value of cash flows received during retirement years. We then examine how a number of risk factors including interest rates, disease diagnosis, and mortality risks impact benefit value. Specifically, we utilize principal component analysis in order to assess both interest rate and mortality risk. We then conduct numerical studies to examine how such risks range over distinct income and demographic groups and finally summarize future research directions.

Suggested Citation

  • Yassmin Ali & Ming Fang & Pablo A. Arrutia Sota & Stephen Taylor & Xun Wang, 2019. "Social Security Benefit Valuation, Risk, and Optimal Retirement," Risks, MDPI, vol. 7(4), pages 1-31, December.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:4:p:124-:d:297515
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    References listed on IDEAS

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