On Single Point Forecasts for Fat-Tailed Variables

TL;DR

This paper critiques naive forecasting methods for fat-tailed variables, emphasizing the importance of understanding tail risks, using COVID-19 as a case study, and highlighting the limitations of simple scientific approaches.

Contribution

It identifies common errors in tail risk forecasting and advocates for more nuanced methods, especially in the context of pandemic-related uncertainties.

Findings

Naive evidence-based forecasts often misrepresent tail risks.

Multiplicative nature of phenomena like COVID-19 affects risk management.

Simple scientific methods are insufficient for tail risk mitigation.

Abstract

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).