Heavy-tails in economic data: fundamental assumptions, modelling and analysis

TL;DR

This paper reviews the significance of heavy-tailed distributions in economic data, highlighting their role in explaining rare but impactful market events and discussing recent advances in understanding their mechanisms.

Contribution

It provides an overview of the fundamental assumptions, modeling approaches, and recent analytical developments related to heavy-tails in economic and financial systems.

Findings

Heavy-tailed distributions explain rare market crashes.

Recent research advances improve understanding of heavy-tail mechanisms.

Heavy-tails influence risk management and financial regulation.

Abstract

The study of heavy-tailed distributions in economic and financial systems has been widely addressed since financial time series has become a research subject.After the eighties, several "highly improbable" market drops were observed (e.g. the 1987 stock market drop known as "Black Monday" and on even more recent ones, already in the 21st century) that produce heavy losses that were unexplainable in a GN environment. The losses incurred in these large market drop events did not change significantly the market practices or the way regulation is done but drove some attention back to the study of heavy-tails and their underlying mechanisms. Some recent findings in these context is the scope of this manuscript.