Monevator
More significantly, Pfau found that no country – not even the US – could replicate William Bengen’s original 4% SWR finding.
Why? Because Bengen, the author of the 4% rule, relied upon a dataset containing better US historical returns than the one used by Pfau. Both archives are well credentialed. Both offer a version of the past. But the differences between the numbers reveal there’s nothing inevitable about the 4% rule – even if you invest solely in the US.
Should US assets exhibit a moderately worse sequence of returns in the years ahead than they did in the past, then future American investors may have to anchor on a 3% rule – or something nastier still.
That’s a plausible outcome. Hence retirement researchers have turned to international datasets and Monte Carlo studies to challenge the assumptions embedded in the US’s exceptional past returns. (I’ve previously used one such database to determine a World SWR of 3.5%.)
Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. The name comes from the Monte Carlo Casino in Monaco, where the primary developer of the method, mathematician Stanisław Ulam, was inspired by his uncle’s gambling habits.
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