Fat and Heavy Tails in Asset Management

Abstract

In this article, the authors explain non-normal probability distributions and the reasons it is important to properly model the tails of one or more distributions in applications to asset management. The authors illustrate the types of quantitative models needed in asset management and provide some basic concepts on random variables and stochastic processes useful to understand non-normal models. After having reviewed the stylized facts of log-returns, the authors describe, in nontechnical terms and with only a few formulas, univariate and multivariate non-normal models that are able to explain the fat (and heavy) tails empirically observed in the distribution of asset and portfolio log-returns.