Predicting market instability: New dynamics between volume and volatility

TL;DR

This paper investigates the complex relationship between trading volume and volatility, revealing new scaling laws and demonstrating that volume can effectively predict maximum future volatility.

Contribution

It uncovers new power-law scaling in volume-conditional volatility and shows that combining volume and volatility improves prediction of extreme market movements.

Findings

Volume-conditional volatility follows a power-law with exponential cutoff.

Distributions of volume-conditional volatility collapse onto a single curve when scaled.

Volume is a strong predictor of maximum future volatility.

Abstract

Econophysics and econometrics agree that there is a correlation between volume and volatility in a time series. Using empirical data and their distributions, we further investigate this correlation and discover new ways that volatility and volume interact, particularly when the levels of both are high. We find that the distribution of the volume-conditional volatility is well fit by a power-law function with an exponential cutoff. We find that the volume-conditional volatility distribution scales with volume, and collapses these distributions to a single curve. We exploit the characteristics of the volume-volatility scatter plot to find a strong correlation between logarithmic volume and a quantity we define as local maximum volatility (LMV), which indicates the largest volatility observed in a given range of trading volumes. This finding supports our empirical analysis showing that volume is an excellent predictor of the maximum value of volatility for both same-day and near-future time periods. We also use a joint conditional probability that includes both volatility and volume to demonstrate that invoking both allows us to better predict the largest next-day volatility than invoking either one alone.