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Econometric Models for Computing Safe Withdrawal Rates

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  • Prendergast, Michael

Abstract

This paper describes a methodology for estimating safe withdrawal rates during retirement that is based on a retiree’s age, risk tolerance and investment strategy, and then provides results obtained from using that methodology. The estimates are generated by a three-step process. In the first step, Monte Carlo simulations of future inflation rates, 10-year treasury rates, corporate bond rates (AAA and BAA), the S&P 500 index values and S&P 500 dividend yields are performed. In the second step, portfolio composition and withdrawal rate combinations are evaluated against each of the Monte Carlo simulations in order to calculate portfolio longevity likelihood tables, which are tables that show the likelihood that a portfolio will survive a certain number of years for a given withdrawal rate. In the third and final step, portfolio longevity tables are compared with standard mortality tables in order to estimate the likelihood that the portfolio outlasts the retiree. This three-step approach was then applied using both a Monte Carlo random walk model and an ARIMA/GARCH model based upon over 100 years of monthly historical data. The end result was estimates of the likelihood of portfolio survival to mortality for over 500,000 retiree age/sex/portfolio/withdrawal rate combinations, each combination supported by at least 10,000 Monte Carlo economic simulation points per model. Both models are supported by 100 years of historical data. The first model is a random walk with step sizes determined by bootstrapping, and the second is an ARIMA/GARCH regression model. This data is analyzed to predict safe withdrawal rates and portfolio composition strategies appropriate for the late 2021 economic environment.

Suggested Citation

  • Prendergast, Michael, 2022. "Econometric Models for Computing Safe Withdrawal Rates," OSF Preprints jd2xg_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:jd2xg_v1
    DOI: 10.31219/osf.io/jd2xg_v1
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    References listed on IDEAS

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    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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