  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.
