How Monte Carlo Can Understate Retirement Income Risk Compared To Historical Simulation

Kitces published a great article co-authored by Derek Tharp and Income Lab co-founder, Justin Fitzpatrick. In the article they explore how Monte Carlo simulation can actually *understate* retirement income risk relative to historical simulation at probability of success levels commonly used (e.g., 70% to 90%).

Here are a few of the main points:

• While future returns are unknowable, analytic methods such as Monte Carlo and the use of historical returns can both provide advisors more confidence that their clients’ retirement spending will be sustainable.
• Contrary to popular belief, Monte Carlo simulation can actually be less conservative than historical simulation at levels commonly used in practice.
• Incorporating tools that use a range of simulation types and data could provide more realistic spending recommendations for clients.

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Justin Fitzpatrick, PhD, CFA, CFP

Justin Fitzpatrick is President and Co-Founder of Income Lab, retirement income planning software used by thousands of financial advisors. He developed the guardrails-based approach to retirement income distribution after a decade in financial services at Jackson and seven years in academia at MIT, Harvard, and UCLA. His research on adjustment-based planning has been published on Kitces.com, ThinkAdvisor, AdvisorPerspectives, and FinancialPlanning Magazine.

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