Paper: On single point forecasts for fat-tailed variables


We discuss common errors and fallacies when using naive “evidence based” empiricism and point forecasts for fat-tailed variables, as well as the insufficiency of using naive first-order scientific methods for tail risk management.

We use the COVID-19 pandemic as the background for the discussion and as an example of a phenomenon characterized by a multiplicative nature, and what mitigating policies must result from the statistical properties and associated risks. In doing so, we also respond to the points raised by Ioannidis et al. (2020).

Link to Paper –…

[YouTube] Explaining both the XIV trade and why forecasting is BS

Antifragile explains why understanding x is different from f(x) the payoff or exposure from x. Most of the harm/gains come from f(x) being convex or concave not understanding x. Forecasting is off an average, and average is for academics and other morons.

This video illustrates the point with XIV that went bust while being correct about volatility –and why people who make money are usually wrong.