A Copula Approach On The Dynamics Of Statistical Dependencies In The US Stock Market
The authors 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, they 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. They compare the tail dependence to the market's average correlation level as a commonly used quantity and disclose a nearly linear relation.