A Copula Approach on the Dynamics of Statistical Dependencies in the US Stock Market
AbstractWe 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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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1102.1099.
Date of creation: Feb 2011
Date of revision: Mar 2011
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-02-19 (All new papers)
- NEP-CIS-2011-02-19 (Confederation of Independent States)
- NEP-ECM-2011-02-19 (Econometrics)
- NEP-ETS-2011-02-19 (Econometric Time Series)
- NEP-FMK-2011-02-19 (Financial Markets)
- NEP-MST-2011-02-19 (Market Microstructure)
- NEP-RMG-2011-02-19 (Risk Management)
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