A Note on Jump Atlas Models

TL;DR

This paper studies jump-augmented rank-dependent models for stock market weights, demonstrating long-term stability and analyzing how jumps affect the properties of continuous models through simulations.

Contribution

It introduces a stability result for jump-augmented rank-dependent models and examines the impact of jumps on model properties via simulations.

Findings

Long-term stability of jump models is established.

Certain properties of continuous models are preserved after adding jumps.

Simulations show how jumps influence model behavior.

Abstract

The market weight of a stock is its capitalization (cap) divided by the total market cap. Rank these weights from top to bottom. The capital distribution curve is a plot of weights versus ranks. For the US stock market, it is linear on a double logarithmic scale, and stable with respect to time (Fernholz, 2002). This property has been captured by models with rank-dependent dynamics: Each stock's cap logarithm is a Brownian motion with drift and diffusion coefficients depending on its current rank (Chatterjee, Pal, 2010). However, short-term stock movements have heavy tails. One can add jumps to Brownian motions to capture this. Observed time stability follows from a long-term stability result, stated and proved here. Via simulations, we find which properties of continuous models are preserved after adding jumps.