A Copula Approach on the Dynamics of Statistical Dependencies in the US Stock Market

TL;DR

This paper investigates the dynamic statistical dependencies among S&P 500 stocks from 2007 to 2010 using a copula-based method, revealing strong tail dependencies that surpass Gaussian correlations and relate linearly to average market correlation.

Contribution

It introduces a copula approach to analyze tail dependencies in stock market data, highlighting the strength and market relevance of these dependencies during a specific period.

Findings

Tail dependence is stronger than Gaussian correlation.

Tail dependence correlates linearly with average market correlation.

Dependencies are particularly strong in the distribution tails.

Abstract

We analyze the statistical dependency structure of the S&P 500 constituents in the 4-year period from 2007 to 2010 using intraday data from the New York Stock Exchange's TAQ database. With a copula-based approach, we find that the statistical dependencies are very strong in the tails of the marginal distributions. This tail dependence is higher than in a bivariate Gaussian distribution, which is implied in the calculation of many correlation coefficients. We compare the tail dependence to the market's average correlation level as a commonly used quantity and disclose an nearly linear relation.