Cross-correlations between volume change and price change

TL;DR

This paper investigates the relationship between volume change and price change in financial markets, revealing power-law cross-correlations and estimating the tail exponent of volume growth rate distributions across multiple indices.

Contribution

It introduces a novel analysis of volume growth rate correlations with price changes and proposes a new method to estimate the tail exponent of volume change distributions.

Findings

Power-law cross-correlations between |R| and | ilde R| identified

Tail exponent   estimated for volume growth rates

Inverse cubic law observed across multiple financial indices

Abstract

In finance, one usually deals not with prices but with growth rates $R$, defined as the difference in logarithm between two consecutive prices. Here we consider not the trading volume, but rather the volume growth rate $\tilde R$, the difference in logarithm between two consecutive values of trading volume. To this end, we use several methods to analyze the properties of volume changes $|\tilde R|$, and their relationship to price changes $|R|$. We analyze $14,981$ daily recordings of the S\&P 500 index over the 59-year period 1950--2009, and find power-law {\it cross-correlations\/} between $|R|$ and $|\tilde R|$ using detrended cross-correlation analysis (DCCA). We introduce a joint stochastic process that models these cross-correlations. Motivated by the relationship between $| R|$ and $|\tilde R|$, we estimate the tail exponent ${\tilde\alpha}$ of the probability density function $P(|\tilde R|) \sim |\tilde R|^{-1 -\tilde\alpha}$ for both the S\&P 500 index as well as the collection of 1819 constituents of the New York Stock Exchange Composite index on 17 July 2009. As a new method to estimate $\tilde\alpha$, we calculate the time intervals $\tau_q$ between events where $\tilde R>q$. We demonstrate that $\bar\tau_q$, the average of $\tau_q$, obeys $\bar \tau_q \sim q^{\tilde\alpha}$. We find $\tilde \alpha \approx 3$. Furthermore, by aggregating all $\tau_q$ values of 28 global financial indices, we also observe an approximate inverse cubic law.