How money got its tail (not too light; not too heavy; but “just so”) Available to Purchase

The purpose of this paper is to explore the theoretical basis for heavy‐tailed asset‐return distributions.

Through a simple model of asset‐price formation, one can formulate the asset‐return random variable, ln (P_t/P_t−1), as a constant plus the natural log of a ratio of Bernoulli proportions. This random variable admits of different approximations, whose distributions may be studied analytically.

The paper finds that for two reasonable approximations to the asset‐return random variable, the tails are approximately exponential. This suggests that the Gaussian assumption provides a poor “starting point” for asset‐pricing models, and empirically validated heavy‐tailed behavior is likely the result of time‐dependent components in the tail parameters.

The editorial offers a theoretical analysis of asset‐return distributions using parsimonious modeling assumptions.