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The real reason that fat-tails aren't widely used isn't ignorance, it's because they are tricky to model and work with and have there own unexpected results.

While assuming normality will very often lead you to underestimate risk, fat tails can easily cause you to over estimate your risk.

Or think about the case of "robust" regression in which errors in a linear model are assumed to be from a fat tailed distribution like Student's T. This means that your model is not suprised by outliers. The consequence of this is that your model assumes that mode data is near the center and ignores extreme observations.

As a philosophical device fat tails are very helpful and interesting, but as a practical tool for modeling they are far from a panacea




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