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

2011/02/28 by Michael C. Münnix, Rudi Schäfer · 1 citation
Economics, Econometrics and Finance · #q-fin.ST

paper · pdf · doi:10.1016/j.physa.2011.06.032

arxiv created 2011/03/03 · arxiv updated 2015/05/27

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.

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